23 ms·

The decline of computers as a general-purpose technology

309 points by matt_d 6y ago

ArtWomb 6y ago
Fab numbers are astronomical. "TSMC recently announced plans for a $19.5B 3nm plant for 2022". How can the next computing paradigm even take off when all the oxygen is depleted in chasing "proven" technologies?
- 6pac3rings 6y ago
  How is the "Cage of Birds" experiment going at TSMC?
  - aYsY4dDQ2NrcNzA 6y ago
    Could you give some more search terms? Google isn’t returning anything.
    - 6pac3rings 6y ago
      Search for Nikkei article on TSMC and growth plans. Look for "cage" and "bird" search terms at that source.
- the8472 6y ago
  What are the other options you would want to replace those proven technologies with? Sure, there are better semiconductor materials but they're expensive and already used where it makes economical sense. Nanotubes or graphene can't be handled at scale yet. Superconductors need huge contraptions for cooling. Using organic molecules is messy, see OLED. Optical parts might help, but that is difficult with silicon which brings us back to the other semiconductor options. Spintronics are already being put to use but those things have eye-watering prices. Look at intel, even incremental changes can go wrong and cost you competitiveness.
  - ArtWomb 6y ago
    Classic innovator's dilemma. I personally am biased to silicon optics after seeing a few impressive demos. Albeit in laboratory conditions, under controlled settings ;) Rise of the learning machines https://www.nature.com/articles/s41566-020-00748-w https://www.nature.com/articles/s41566-020-00748-w
- jlokier 6y ago
  That's not as much money as it sounds for the industry. Apple revenue is more than that each month. There's enough money around. So it seems unlikely that the cost of TSMC silicon fabs would be the obstacle to others developing alternative computing paradigms.
h2odragon 6y ago
Where is all the unpaid innovation happening, where are people creating for the joy of creating? 8 bit micro controllers and little SoCs.
- op03 6y ago
  Here you go - https://twitter.com/sndrv/status/1356884973979115522 https://twitter.com/sndrv/status/1356884973979115522
- black_puppydog 6y ago
  Why does it have to be unpaid? https://twitter.com/mntmn https://twitter.com/mntmn
- jonathanaird 6y ago
  It’s happening in the tools for thought community on Twitter. It’s more about the software layer and innovations in human computer interaction design. A lot of the ideas from the 60’s and 70’s are having a resurgence like Memex, backlinks, moldable dev environments etc.
- xkeysc0re 6y ago
  I bought a wi-fi modem for my Commodore 64. It's everywhere, you just gotta poke around
- AnimalMuppet 6y ago
  Some people literally created an online dumpster fire. You send it an email, it prints it out, and the paper goes into a flaming dumpster. It then sends you the video of this happening. Why? Just because they thought it would be cool. (Er, hot.) https://hey.science/dumpster-fire/ https://hey.science/dumpster-fire/
rektide 6y ago
> We do mean that the economic cycle that has led to the usage of a common computing platform, underpinned by rapidly improving universal processors, is giving way to a fragmentary cycle, where economics push users toward divergent computing platforms driven by special purpose processors. computing in my view is rapidly subliming away into the cloud. whether users will have much computer at all in front of them is questionable, or perhaps semi-vestigial. work/applications are headed into data centers. want photoshop today? even the old-fashioned install & run it yourself version has major cloud connectivity. even within the world.of.consumer electronics, I don't see specialization as a trend. video game consoles have tended towards "exactly like a pc" over time. cell phones are pc's with the most proprietary/controlled drivers on the planet. 5g base stations are pc software-defined-networks plus gobs of general purpose dsps/sdrs. in the smaller computing/devuce world, arm and espressif and soon risc-v are cutting out larger & larger swarths. thus far ai processors have had fairly wide ability to operate models in a cross-architecyure fashion. make sure you can run onnx or tf-lite. big only marginally customized gpus still have a huge presence. there is variety here but remarkably little end user differentiation. where are the specialized systems this write up talks to? where are the fragmented systems? the door is opening as we try to re-learn how to make silicon foundries, how to do open source chip making, but I haven't seen what's being talked about here, specialization, bifurcation of capabilities. but cloud? cloud is murdering the shit out of general purpose computing. applicationization of software turns software effectively hard to end users. we have no power, no ability to adapt or change or see the computing. it's 100% what is given to us. every system is 100% special/specialized, and in the post Pax Intertwingularis death of the api & interoperability, that means radical rigidity. systems are radically more specialized & in&general, but it's not a hardware problem like this paper asserts and it's not something on thebhorizon: it's already happened, it's already made the general.purpose computer obsolete, and it's at the software level, it's about where most of the world's computing is run, on special systems, on the clouds.
- 082349872349872 6y ago
  "I think there is a world market for maybe five clouds." — not Thomas Watson
brainless 6y ago
This is what I personally feel is just missing these days. I was never part of the leading edge of hobby computing. I was very far off, geographically, growing up a teenager in India. But back then, 1998 - 2006 ish, I feel the Internet was a lot more about experiments. I would read up on some software or hardware group all the time. LUGs were way popular. I used to be on chat rooms from MIT or other unis every other day. Nowadays most people I see of that age group are happy with a YouTube video on their smartphones. General purpose of computers is simply not visible. Not even to the level that I, a curious teenager, had experienced in those years from so far in India. We have too many tall-walled gardens now. Update: I should add I am happy with how powerful computers are these days, but they are not for the purpose of learning or tinkering the actual device or its software. It is just way to consume media for the mainstream.
- fsflover 6y ago
  > Nowadays most people I see of that age group are happy with a YouTube video on their smartphones. General purpose of computers is simply not visible. Have a look at GNU/Linux phones recently developed: https://en.wikipedia.org/wiki/Librem_5 https://en.wikipedia.org/wiki/Librem_5 https://en.wikipedia.org/wiki/PinePhone https://en.wikipedia.org/wiki/PinePhone
- blowfish721 6y ago
  From my perspective I think it might feel that way because back then it was mostly tech curious people who used the internet. Now it’s mainstream and everyone is connected which means most of the content is produced for massconsumation. Back when I got my first computer and got online, in around 1996 I think, the computers were still quite expensive and I would have killes to have access to the information and tinkerboards/cheap computers that are available today.
  - harlanji 6y ago
    Maybe we could make an Internet for Age 35+ or so. Remember when one could take over Yahoo chats with Javascript? With containers and stuff, the Internet could be a lot of fun again. App battles in the park by old timers on the chess tables.
blt 6y ago
This seems a little sensationalist to me. The GPU is a general-purpose device optimized for highly parallel workloads. One could just as easily say that a traditional CPU is a special-purpose device optimized for branch-heavy code at the expense of parallel throughput. The current state of GPUs is based on Nvidia's decision to focus on GPGPU instead of being a DirectX accelerator forever. They specifically decided to make the device more general-purpose than it needed to be at the time. Presumably they thought there was a chance to start another virtuous cycle for parallel computing. I don't think we are close to realizing the GPU's potential as a general-purpose device. Imagine the kind of software we will have when every phone has the equivalent of today's biggest desktop GPU inside it. The next revolutionary general-purpose device will probably feel like a niche product at first, just as the 3Dfx Voodoo did in 1996.
nathias 6y ago
it looks like we're going back to terminals (renting cloud resources), where owning your own computer will be an increasingly niche hobby
- tonyedgecombe 6y ago
  In some ways I don't mind that. If all the normal people are off using their terminals then perhaps workstations will shift back towards supporting advanced users.
- brucehoult 6y ago
  That was a decade or more ago. Now we're getting heavier and heavier JavaScript and WebASM running locally on our "dumb terminals" -- and with them using local permanent storage too.
cblconfederate 6y ago
I dunno, PC sales are growing a lot this year, and with remote work, they are bound to stay that way for the next years
dschuetz 6y ago
It's declining perhaps, yes, but it's not there yet. Because modern workloads shifted mostly towards gaming, crypto-mining and web browsers doesn't mean that general-purpose computers declined. It only means they haven't developed very fast, but that is thanks to Intel and Microsoft. In my opinion, the OS needs to go all virtual+distributed for general-purpose to survive. Or else all hardware will become web browser accelerators. Even now all browsers already have their own hardware drivers for video.
- spoonjim 6y ago
  What I find is that people of my generation have some basic idea of at least some computer jargon because of what we had to learn just to get our PCs working in the 80s and 90s. What in fuck's name is a parity bit? We learned because we wanted to get our modems configured to play Doom together when our moms weren't on the phone. I'm not talking about people who went into computers, I'm just talking about the people who used them. Nowadays everything just works so damn well. My phone works with my printer without any driver installation let alone mucking around. So nobody has any idea how anything works.
  - dschuetz 6y ago
    The people who grew up knowing what a parity bit was, are in the IT consulting business now, myself included. I think that's how things naturally change. IT and computer hardware has become that much complex and popular at the same rate, you now need people and abstraction for that. It doesn't have to be a bad thing though.
    - asdff 6y ago
      I really don't think its any more complex than it was 20-30-40 years ago. At the end of the day, none of this GUI abstraction does anything more than what the computer can do at the command line, and that's practically been etched in stone at this point—some of these bash commands are older than my boomer parents. Instead, people just don't have to bother learning beyond the abstraction to get work done anymore. It's just like cars: you don't need to know how to drive a stick shift and all the mechanical theory behind shifting gears smoothly if you have an automatic transmission instead.
niftich 6y ago
Note that the article is about innovation and performance gains in general-purpose processors slowing, and about the increasing shift onto specialized computing engines by those who seek further performance gains for specific workloads. This was foreshadowed with the Netburst not being able break 4 GHz in 2004-2005, and CPUs having to shift to multi-core. This bought "classic" CPUs more time, but CUDA showed up in 2007 and GPUs went from strictly specialized computing engines to general-purpose (in research, if not yet in the home). CPUs have also been steadily gaining SIMD extensions. Now GPUs are showing promise for NN workloads, but in environments where the stack is tightly controlled, NN co-processors are showing up. This is because tightly controlling the stack has the benefits of being able to optimize and harmonize software and hardware, and interop outside of the stack (and in some cases, stack longevity) is not a factor. The article isn't truly about how more and more computing environments tightly control their stack, but that mechanism does play a part in the design choices that result.
- pwdisswordfish6 6y ago
  You must be new here to expect Hacker News to read an article before commenting.
- petermcneeley 6y ago
  The article discusses the current trend but you hint at the possibility of a trend reversal. The CPUs are more and more multicore with wider SIMD. Many now have dynamic clock rates or low clock rates. The GPUs which use to actually have fixed functions now more and more are just GPGPUs. This to me is a convergence.
d--b 6y ago
The fact that I can use Python to program a game on a raspberry pi, and use the same language to train GPT-3 is evidence that general purpose computing has not been declining...
- lenkite 6y ago
  Python cannot be used for accessible mobile apps AFAIK.
coldtea 6y ago
If by decline most meant "computers being more affordable, powerful, and with more languages, compilers, documentation, FOSS and proprierary software available than ever, not even close to the 'heyday' of general-purpose computer era they have in mind in the 80s or so" then they're right. Though I think they mean "I want a specific vendor to make a product they don't want to make". (This is not about TFA, which examines a more well dedined "universal vs specialized processor" dichotomy, but about the frequent laments about general purpose computing).
brucehoult 6y ago
What the authors have completely missed in their analysis is the rapidly-growing trend of specialising standard general-purpose CPUs -- that is adding special-purpose instructions to general purpose CPUs instead of adding special-purpose processors. This process started a couple of decades ago, adding instructions to assist in the calculation of hashes and encryption, and relatively narrow SIMD parallel processing to assist multimedia. Now virtually all high volume general purpose processors have such instructions. If you count simple floating point add, subtract, multiple, divide as special purpose then this process started 50+ years ago. The advantage of doing this is less added area and power consumption, and the ability to mix special purpose and general purpose operations at a much finer-grained level. Sometimes its worth copying a few MB of data across to a GPU's local RAM, downloading a special program to it, and then copying the results back. The number of potential special purpose operations that are useful to someone is probably unbounded. But each one might be useful to only a small number of people. It's not feasible to just keep on adding everything someone thinks of to the volume leader mass-market processor. Three related things are happening to help with this: 1) adding reconfigurable hardware to a general purpose processor or embedding the processor into reconfigurable hardware. Here we have Xilinx "Zynq" and MicroChip "PolarFire SoC" with ARM and RISC-V (respectively) hard CPU cores inside an FPGA. We also have Cypress PSoC which I believe is more like adding a small amount of reconfigurable hardware on the side of a conventional CPU core. If the performance needs of the general-purpose part of the processing are relatively low then you can use a "soft core" CPU built from the FPGA resources themselves. Each FPGA vendor has had their own custom instruction set and CPU core, but now people are moving more and more to instructions sets and cores they can use on any FPGA -- chiefly RISC-V. 2) making custom chips with a standard CPU core augmented with a few custom instructions / execution units. Again, much of this activity is centred around RISC-V though ARM has announced support for this with one or two of their standard CPU cores, initially the Cortex A35. Going into full production of a chip like this has costs in the low millions of dollars, with incremental unit costs as low as $1 to $10. Small numbers of custom chips (100+) can be made for $5 to $500 each depending on the size of the chip and the process node -- bigger, slower nodes are cheaper. 3) adding special purpose instructions that can be more flexibly applied to a larger range of problems. The main contender here is support for "Cray-style" processing of (possibly) long vectors of flexible length. If appropriately designed the same program can run at peak efficiency (for that chip) on CPUs with vastly different vector register lengths. This is in contrast to traditional SIMD where the program has to be rewritten every time a CPU is made with longer vector registers -- and it is very inconvenient to deal with data set sizes that are not a multiple of the vector length. If suitable primitives are included for predication of vector elements and divergent and convergent calculations then such a vector processor can run the same algorithms as GPUs (e.g. directly compiling CUDA and OpenCL to them). CPUs with sufficiently long vector registers can then compete directly in performance with GPUs on GPU-style code. All while staying tightly integrated with general purpose computations. ARM SVE and the RISC-V V extension are the examples of this, with I think the RISC-V version being the more flexible and forward-looking.
SiVal 6y ago
There's no doubt that we're getting an ever-widening variety of clients, but this "general purpose" notion is a bit slippery. I remember when "general purpose" meant that not only did computers crunch numbers for science and engineering, but they could finally handle more general business computing such as accounting and text-heavy business data processing, which was much larger than number crunching--more general purpose. Then we got microcomputers, and we added consumers and individual employees to "general purpose", and "word processing, word processing, word processing, and spreadsheets" became the new definition of general purpose computing, dwarfing big iron data processing. Then laser printers and desktop publishing, then Photoshop, CDs and "multimedia", and then The Internet (meaning the web) exploded and general purpose computing suddenly meant email, instant messaging, and so many new home pages that Yahoo could hardly list them all. In the Internet Boom, nobody defined general purpose computing as mostly word processing, much less business data processing, and forget about scientific calculating. Fast forward a couple more decades, and general purpose computing is streaming YouTube celebs, texting, binge watching streaming "TV", getting most of your news managed by Big Tech censors so you can't "misunderstand", keeping up with ever-streaming timelines of your 900 closest friends, going to work or school via Zoom, doing most of your shopping.... And where is word processing in this general purpose computing? Probably bigger than ever, but now buried in the "other" category because newer components of general purpose computing dwarf it. So if some of this is done with a laptop, and some with a "phone", and some with a flat screen in your car, and some with your TV remote, and some with an iPad...I suspect that "computers" could easily be described as either declining or growing for general purposes depending on which of many reasonable definitions you employed.
- brucehoult 6y ago
  You are trying to define tasks as "general purpose". They aren't and never can be. The point is that the basic modern computer design works well (enough) for almost all of those tasks. For the most part all that you need is the basic operations on 32 or 64 bit "integers": move, compare, add, subtract, and, or, xor, shift left, shift right. With this you can do spreadsheets, word processing, database, accounting, desktop publishing, web browsing, email, instant messaging, decoding MP3 audio and JPEG photos and MPEG video. And show it all on a GUI on a high resolution bitmapped display. You can increase the efficiency of some of those tasks with hardware and special purpose instructions for floating point arithmetic (though spreadsheets for example hardly benefit) and SIMD processing for media. See my other (top level) post. But you don't need them, especially if you have many general purpose CPU cores. Don't forget the original Macintosh did basically all of those tasks with just a simple integer-only CPU running at 8 MHz. No FPU, no GPU, no media instructions. A modern multi-GHz CPU can do all those tasks acceptably using only simple integer instructions as long as you don't insist on very high resolution video (and games) at high frame rates.
konschubert 6y ago
The economy is finally catching up with building use cases around what a computer can do. The result is that special-purpose computing is growing, and general-purpose computing is on the decline.
mathis 6y ago
Link was broken for me. This one works: https://dl.acm.org/doi/10.1145/3430936 https://dl.acm.org/doi/10.1145/3430936
JoeAltmaier 6y ago
"Decline" as in "Still growing but not as much as some other things". This analysis sounds like sour grapes. "Phones get all the love!" doesn't actually mean that general-purpose computing is declining. Just the opposite. It continues to grow in power as it always has.
- tartoran 6y ago
  I agree and the pool of tinkerers and intelectually curious has been growing steadily. There are so many free resources available that the barrier to entry has lowered enormously. However, the enormous growth has gone towards consumption and that is not necessarily a bad thing.
williesleg 6y ago
Part of The Great Reset. Computers are too powerful. Just give them a locked-down iPad. Of course, we'll have India write all the code, they do it cheap.
solinent 6y ago
I think as computers fail to get faster, we won't be able to improve performance without making application specific logical units, however, we may still be able to make it general purpose.
- shadowgovt 6y ago
  What I'm most interested in is what the trends will be in how those specialized circuits are developed. Circuit design has been marching steadily along for decades, and between the tools available to allow non-specialists to go from abstract hardware description to image-ready chip specification and the ever-cheapening, ever-broadening capabilities of custom chip fab, are we approaching an era where fabricating mid-range specialized circuits is on par with the difficulty of writing and compiling software, only a bit slower?
- asdff 6y ago
  I'm waiting for automatic code optimization. Write the code going from an input to an output however you like, show the computer your input and output, have it rewrite the most optimal (and probably very weird looking) code possible to go from your supplied input to your supplied output, and reap the efficiency rewards to mastering a stack of arcane low level code reference books without spending the thousands of hours of your precious and limited free time to do so. That would be a huge leap imo.
mannykannot 6y ago
The article is factually correct, but I feel that the analysis is missing some of the historical context. Here's a quite probably strained analogy to cosmology: Microprocessors and CMOS upended the computer industry, to the extent that a few years later, all the big companies in computing, up to that point, were in precipitous decline (even IBM, which embraced the new world order with the PC, only delayed this reckoning.) In those days, microprocessors and DRAM alone were the cutting edge of technology, and they opened up all sorts of possibilities (though this also depended on some additional special-purpose equipment, notably for graphics and networking.) One might draw an analogy here to the Big Bang. What happened next was like a period of cosmic inflation, in which Moore's law and Dennard scaling (plus fiber optics and the wiring of the world) created exponential growth. As in cosmology, we end up with a much bigger but quite uniform universe, in this case because the growth of the basic technology alone was enough fuel for all the innovation we could come up with. It was only after inflation that the universe became really interesting. It is still overwhelmingly hydrogen, but it has differentiated: there are also galaxies, stars, planets and people. So, the long-delayed conclusion to this analogy: innovation in computing is now more mature, but it is not shrinking, it has diversified - and there are still 10^n (for some large n) 8- and 16-bit microprocessors being made, and there are still hobbyists doing clever things with what is now basic, even primitive, hardware (that, not so long ago, was unattainable) - but we also have emerging technologies (machine translation and autonomous vehicles, for example) that quite a few people, not so long ago, assumed would be forever beyond the capabilities of mere machines.
- GLGirty 6y ago
  A had a similar reaction to the article, and I appreciate your cosmic analogy. The metaphor that came to my mind was the long persistence of steam power after electric motors were invented. Initially, 'going electric' meant replacing your one giant steam boiler with one giant electric motor, and doing everything else the same way. The factory remained organized around the drive shafts and pulleys and mechanical distribution systems. There was little advantage. It took decades of experimentation to gain the insights of how many small motors and task lighting could allow a factory to be optimized for task flow, not power distribution. And then it took longer for those insights to diffuse through slow human networks. Hardware has developed so fast that software had no time or incentive to mature. Each decade's tech just gets ossified into the stack because hardware was making the stack faster at a better rate than human insight. Every few decades, we get lucky and an invention happens when the ecosystem can use it... so we get compilers and sql and automated tests. But then other insights like immutable data structs just stay niche. I hope that as hardware progress flattens, opportunities emerge for better software paradigms. Maybe this will coincide with the craft of software becoming introspective about it's myriad social issues.
kristianpaul 6y ago
Yeah computers are everywhere now “ Collectively, these findings make it clear that the economics of processors has changed dramatically, pushing computing into specialized domains that are largely distinct and will provide fewer benefits to each other. Moreover, because this cycle is self-reinforcing, it will perpetuate itself, further fragmenting general purpose computing. As a result, more applications will split off and the rate of improvement of universal processors will further slow.”
Zigurd 6y ago
"Personal" computers were subversive when first introduced. They were designed for complete control by an individual. They were also, initially, not networked. When networks first appeared it was with the assumption that the user community of the network did not have a persistent high level of malicious security threats. In a kludgy, gradual way personal computers were tamed. This was inevitable. If you network completely general purpose, user-controlled machines in an institutional environment you will get security chaos. Some of this, though, like putting digital rights management into personal computers, was tragically evil. What we are left with is neither here nor there: Computers are not secure. Our communications are not secure and are under constant surveillance. And we do not control machines we supposedly own. Lose, lose, lose. You cannot have security and surveillance and central control of "personal" devices. You cannot protect "digital rights" and sell entertainment content to users that truly control their personal computers.
mattkevan 6y ago
Thing is, in the ‘olden days’, the only thing you could do with a home computer was tinker - the job of a computer was to be a computer but little else. And the number of people interested in doing that was small. Today, the job of a computer is everything. Computers are in everything and do everything. They are our interface to the world. Their value is what they enable - writing, tax returns, video consumption, gaming and everything else, not the fact they’re a ‘computer’. For those of us who want a computer to tinker with, we’ve never had it so good. There’s so many more options than there’s ever been for hackable tech. Just because most people don’t want to do that doesn’t mean they’re wrong, it just means they have different priorities. I love the fact I don’t have to muck about with my iPhone to get it to work just as much as I love mucking about with my raspberry pi to get it to work.
- deleted 6y ago
  [deleted]
- Terretta 6y ago
  Look at cars, consider both tinkering and “right to repair, but with no guarantee on know-how”.
- higerordermap 6y ago
  But most tinkering journeys do not start with dedicated hardware. They start with hardware intended for something else, generally on a quest to solve some problem or do something cool..
  - shadowgovt 6y ago
    The evolution of graphics cards into CUDA machines for machine learning applications is one example of this phenomenon.
- wayneftw 6y ago
  > I love the fact I don’t have to muck about with my iPhone to get it to work... Imagine being able to quickly and easily add your own custom programs to your iPhone without asking anyone's permission though? Also, what if - as the owner of that device - you had full control over it's capabilities like you do with a desktop computer? Adding these abilities certainly would not be the downfall of phone ecosystems as we know them. People that want convenience wouldn't leave their app store. The fact that they are our interface to the world is exactly the reason why we need to take control of them away from the Apple/Google oligarchy. People need to stop comparing these devices to gaming consoles, which don't impact the world even a fraction as much as smartphones, when we're talking about making new rules for running smartphone platforms.
ip26 6y ago
This fragmentation means that parts of computing will progress at different rates. This will be fine for applications that move in the 'fast lane,' where improvements continue to be rapid, but bad for applications that no longer get to benefit from field-leaders pushing computing forward, and are thus consigned to a 'slow lane' of computing improvements. This transition may also slow the overall pace of computer improvement, jeopardizing this important source of economic prosperity. This is perceptive, but also red ocean thinking. Moving ML to GPU is only bad for other workloads if CPU languishes as a result. But I think there's reason to believe it's a blue ocean. The more performance ML gets out of GPU, the more money the ML business can pour into semiconductors, batteries, EDA, and so forth. CPU benefits from all of these. I think you can see this at work in the laptop market. Cell phone R&D has driven battery technology. Server R&D has driven efficient high-performance x86 cores. Both have driven power-efficient process nodes. Put them together and you can make incredible laptops.
carapace 6y ago
To some extent this is just the Futumura Projections writ large, eh? ( https://en.wikipedia.org/wiki/Partial_evaluation#Futamura_projections https://en.wikipedia.org/wiki/Partial_evaluation#Futamura_pr... ) Anyway, I don't see the problem. I don't see how e.g. better GPUs cannibalize the performance of general-purpose CPUs, and I doubt that the market for general purpose computing isn't already saturated. You can buy CPUs for a couple of bucks (little ones), you have a supercomputer in your pocket (phone), and people only buy fancy chips to play video games.
jackcosgrove 6y ago
I see a connection between this and the troubles Intel is facing, and the general decline of CISC processors.
ksec 6y ago
This is a very hardware focused overview of technology. To summarise, Moore's Law is dead. Specialise processors takes over. And they will be owned by specific Companies. That is all true as I have been posting pretty much exactly the same on HN for a few years. The real question is though, does it matter? I am not convinced, at least not yet convinced what we are doing now on a computer is that much different to 20 years ago. Excel, Web Browsing, Media Consumption and Content Creation like Photoshop. The only real recent innovation is Machine Learning that greatly increase productivity in certain niche. And it does not seems to me any of the above activity is going to fundamentally shift. There is nothing in terms of Hardware tech limitation that is holding up performance for further productivity increase, rather it is Software that is not getting much improvement if you consider the worst and best software could have performance different of 100x. To give an example, I could bet with few billion dollar we could create a Hardware GPU that is 80% close to Nvidia GPU or AMD GPU. I am also willing to bet even with 10 Billion dollar funding we cant get a CUDA Compatible Library or Drivers that is 80% of what Nvidia is offering today.
johnnycerberus 6y ago
I really believe that distributed systems will weight more in the future and that virtual machines will be able to abstract both local resources and remote ones and new paradigms will be created in order to program these. We are still in the early days of programming and there's a lot of room for innovation.
magwa101 6y ago
It's a strange article. Portable languages have rendered "general purpose CPUs" moot and allowed for incredible innovation at the chip level. Some may recall that making software run on different chips was a huge deal. Now, you use a portable language and potentially portable APIs that are specific to the purpose at hand. The article ignores that vast access to general capabilities are now available through the internet. With an even higher abstraction that again renders the underlying CPU type irrelevant. Who knows or cares what CPU is serving this page.

309 points by matt_d 6y ago

Fab numbers are astronomical. "TSMC recently announced plans for a $19.5B 3nm plant for 2022". How can the next computing paradigm even take off when all the oxygen is depleted in chasing "proven" technologies?

6pac3rings 6y ago
How is the "Cage of Birds" experiment going at TSMC?
- aYsY4dDQ2NrcNzA 6y ago
  Could you give some more search terms? Google isn’t returning anything.
  - 6pac3rings 6y ago
    Search for Nikkei article on TSMC and growth plans. Look for "cage" and "bird" search terms at that source.
the8472 6y ago
What are the other options you would want to replace those proven technologies with? Sure, there are better semiconductor materials but they're expensive and already used where it makes economical sense. Nanotubes or graphene can't be handled at scale yet. Superconductors need huge contraptions for cooling. Using organic molecules is messy, see OLED. Optical parts might help, but that is difficult with silicon which brings us back to the other semiconductor options. Spintronics are already being put to use but those things have eye-watering prices. Look at intel, even incremental changes can go wrong and cost you competitiveness.
- ArtWomb 6y ago
  Classic innovator's dilemma. I personally am biased to silicon optics after seeing a few impressive demos. Albeit in laboratory conditions, under controlled settings ;) Rise of the learning machines https://www.nature.com/articles/s41566-020-00748-w https://www.nature.com/articles/s41566-020-00748-w
jlokier 6y ago
That's not as much money as it sounds for the industry. Apple revenue is more than that each month. There's enough money around. So it seems unlikely that the cost of TSMC silicon fabs would be the obstacle to others developing alternative computing paradigms.

h2odragon 6y ago

Where is all the unpaid innovation happening, where are people creating for the joy of creating? 8 bit micro controllers and little SoCs.

op03 6y ago
Here you go - https://twitter.com/sndrv/status/1356884973979115522 https://twitter.com/sndrv/status/1356884973979115522
black_puppydog 6y ago
Why does it have to be unpaid? https://twitter.com/mntmn https://twitter.com/mntmn
jonathanaird 6y ago
It’s happening in the tools for thought community on Twitter. It’s more about the software layer and innovations in human computer interaction design. A lot of the ideas from the 60’s and 70’s are having a resurgence like Memex, backlinks, moldable dev environments etc.
xkeysc0re 6y ago
I bought a wi-fi modem for my Commodore 64. It's everywhere, you just gotta poke around
AnimalMuppet 6y ago
Some people literally created an online dumpster fire. You send it an email, it prints it out, and the paper goes into a flaming dumpster. It then sends you the video of this happening. Why? Just because they thought it would be cool. (Er, hot.) https://hey.science/dumpster-fire/ https://hey.science/dumpster-fire/

rektide 6y ago

> We do mean that the economic cycle that has led to the usage of a common computing platform, underpinned by rapidly improving universal processors, is giving way to a fragmentary cycle, where economics push users toward divergent computing platforms driven by special purpose processors. computing in my view is rapidly subliming away into the cloud. whether users will have much computer at all in front of them is questionable, or perhaps semi-vestigial. work/applications are headed into data centers. want photoshop today? even the old-fashioned install & run it yourself version has major cloud connectivity. even within the world.of.consumer electronics, I don't see specialization as a trend. video game consoles have tended towards "exactly like a pc" over time. cell phones are pc's with the most proprietary/controlled drivers on the planet. 5g base stations are pc software-defined-networks plus gobs of general purpose dsps/sdrs. in the smaller computing/devuce world, arm and espressif and soon risc-v are cutting out larger & larger swarths. thus far ai processors have had fairly wide ability to operate models in a cross-architecyure fashion. make sure you can run onnx or tf-lite. big only marginally customized gpus still have a huge presence. there is variety here but remarkably little end user differentiation. where are the specialized systems this write up talks to? where are the fragmented systems? the door is opening as we try to re-learn how to make silicon foundries, how to do open source chip making, but I haven't seen what's being talked about here, specialization, bifurcation of capabilities. but cloud? cloud is murdering the shit out of general purpose computing. applicationization of software turns software effectively hard to end users. we have no power, no ability to adapt or change or see the computing. it's 100% what is given to us. every system is 100% special/specialized, and in the post Pax Intertwingularis death of the api & interoperability, that means radical rigidity. systems are radically more specialized & in&general, but it's not a hardware problem like this paper asserts and it's not something on thebhorizon: it's already happened, it's already made the general.purpose computer obsolete, and it's at the software level, it's about where most of the world's computing is run, on special systems, on the clouds.

082349872349872 6y ago
"I think there is a world market for maybe five clouds." — not Thomas Watson

brainless 6y ago

This is what I personally feel is just missing these days. I was never part of the leading edge of hobby computing. I was very far off, geographically, growing up a teenager in India. But back then, 1998 - 2006 ish, I feel the Internet was a lot more about experiments. I would read up on some software or hardware group all the time. LUGs were way popular. I used to be on chat rooms from MIT or other unis every other day. Nowadays most people I see of that age group are happy with a YouTube video on their smartphones. General purpose of computers is simply not visible. Not even to the level that I, a curious teenager, had experienced in those years from so far in India. We have too many tall-walled gardens now. Update: I should add I am happy with how powerful computers are these days, but they are not for the purpose of learning or tinkering the actual device or its software. It is just way to consume media for the mainstream.

fsflover 6y ago
> Nowadays most people I see of that age group are happy with a YouTube video on their smartphones. General purpose of computers is simply not visible. Have a look at GNU/Linux phones recently developed: https://en.wikipedia.org/wiki/Librem_5 https://en.wikipedia.org/wiki/Librem_5 https://en.wikipedia.org/wiki/PinePhone https://en.wikipedia.org/wiki/PinePhone
blowfish721 6y ago
From my perspective I think it might feel that way because back then it was mostly tech curious people who used the internet. Now it’s mainstream and everyone is connected which means most of the content is produced for massconsumation. Back when I got my first computer and got online, in around 1996 I think, the computers were still quite expensive and I would have killes to have access to the information and tinkerboards/cheap computers that are available today.
- harlanji 6y ago
  Maybe we could make an Internet for Age 35+ or so. Remember when one could take over Yahoo chats with Javascript? With containers and stuff, the Internet could be a lot of fun again. App battles in the park by old timers on the chess tables.

blt 6y ago

This seems a little sensationalist to me. The GPU is a general-purpose device optimized for highly parallel workloads. One could just as easily say that a traditional CPU is a special-purpose device optimized for branch-heavy code at the expense of parallel throughput. The current state of GPUs is based on Nvidia's decision to focus on GPGPU instead of being a DirectX accelerator forever. They specifically decided to make the device more general-purpose than it needed to be at the time. Presumably they thought there was a chance to start another virtuous cycle for parallel computing. I don't think we are close to realizing the GPU's potential as a general-purpose device. Imagine the kind of software we will have when every phone has the equivalent of today's biggest desktop GPU inside it. The next revolutionary general-purpose device will probably feel like a niche product at first, just as the 3Dfx Voodoo did in 1996.

nathias 6y ago

it looks like we're going back to terminals (renting cloud resources), where owning your own computer will be an increasingly niche hobby

tonyedgecombe 6y ago
In some ways I don't mind that. If all the normal people are off using their terminals then perhaps workstations will shift back towards supporting advanced users.
brucehoult 6y ago
That was a decade or more ago. Now we're getting heavier and heavier JavaScript and WebASM running locally on our "dumb terminals" -- and with them using local permanent storage too.

cblconfederate 6y ago

I dunno, PC sales are growing a lot this year, and with remote work, they are bound to stay that way for the next years

dschuetz 6y ago

It's declining perhaps, yes, but it's not there yet. Because modern workloads shifted mostly towards gaming, crypto-mining and web browsers doesn't mean that general-purpose computers declined. It only means they haven't developed very fast, but that is thanks to Intel and Microsoft. In my opinion, the OS needs to go all virtual+distributed for general-purpose to survive. Or else all hardware will become web browser accelerators. Even now all browsers already have their own hardware drivers for video.

spoonjim 6y ago
What I find is that people of my generation have some basic idea of at least some computer jargon because of what we had to learn just to get our PCs working in the 80s and 90s. What in fuck's name is a parity bit? We learned because we wanted to get our modems configured to play Doom together when our moms weren't on the phone. I'm not talking about people who went into computers, I'm just talking about the people who used them. Nowadays everything just works so damn well. My phone works with my printer without any driver installation let alone mucking around. So nobody has any idea how anything works.
- dschuetz 6y ago
  The people who grew up knowing what a parity bit was, are in the IT consulting business now, myself included. I think that's how things naturally change. IT and computer hardware has become that much complex and popular at the same rate, you now need people and abstraction for that. It doesn't have to be a bad thing though.
  - asdff 6y ago
    I really don't think its any more complex than it was 20-30-40 years ago. At the end of the day, none of this GUI abstraction does anything more than what the computer can do at the command line, and that's practically been etched in stone at this point—some of these bash commands are older than my boomer parents. Instead, people just don't have to bother learning beyond the abstraction to get work done anymore. It's just like cars: you don't need to know how to drive a stick shift and all the mechanical theory behind shifting gears smoothly if you have an automatic transmission instead.

niftich 6y ago

Note that the article is about innovation and performance gains in general-purpose processors slowing, and about the increasing shift onto specialized computing engines by those who seek further performance gains for specific workloads. This was foreshadowed with the Netburst not being able break 4 GHz in 2004-2005, and CPUs having to shift to multi-core. This bought "classic" CPUs more time, but CUDA showed up in 2007 and GPUs went from strictly specialized computing engines to general-purpose (in research, if not yet in the home). CPUs have also been steadily gaining SIMD extensions. Now GPUs are showing promise for NN workloads, but in environments where the stack is tightly controlled, NN co-processors are showing up. This is because tightly controlling the stack has the benefits of being able to optimize and harmonize software and hardware, and interop outside of the stack (and in some cases, stack longevity) is not a factor. The article isn't truly about how more and more computing environments tightly control their stack, but that mechanism does play a part in the design choices that result.

pwdisswordfish6 6y ago
You must be new here to expect Hacker News to read an article before commenting.
petermcneeley 6y ago
The article discusses the current trend but you hint at the possibility of a trend reversal. The CPUs are more and more multicore with wider SIMD. Many now have dynamic clock rates or low clock rates. The GPUs which use to actually have fixed functions now more and more are just GPGPUs. This to me is a convergence.

d--b 6y ago

The fact that I can use Python to program a game on a raspberry pi, and use the same language to train GPT-3 is evidence that general purpose computing has not been declining...

lenkite 6y ago
Python cannot be used for accessible mobile apps AFAIK.

coldtea 6y ago

If by decline most meant "computers being more affordable, powerful, and with more languages, compilers, documentation, FOSS and proprierary software available than ever, not even close to the 'heyday' of general-purpose computer era they have in mind in the 80s or so" then they're right. Though I think they mean "I want a specific vendor to make a product they don't want to make". (This is not about TFA, which examines a more well dedined "universal vs specialized processor" dichotomy, but about the frequent laments about general purpose computing).

brucehoult 6y ago

What the authors have completely missed in their analysis is the rapidly-growing trend of specialising standard general-purpose CPUs -- that is adding special-purpose instructions to general purpose CPUs instead of adding special-purpose processors. This process started a couple of decades ago, adding instructions to assist in the calculation of hashes and encryption, and relatively narrow SIMD parallel processing to assist multimedia. Now virtually all high volume general purpose processors have such instructions. If you count simple floating point add, subtract, multiple, divide as special purpose then this process started 50+ years ago. The advantage of doing this is less added area and power consumption, and the ability to mix special purpose and general purpose operations at a much finer-grained level. Sometimes its worth copying a few MB of data across to a GPU's local RAM, downloading a special program to it, and then copying the results back. The number of potential special purpose operations that are useful to someone is probably unbounded. But each one might be useful to only a small number of people. It's not feasible to just keep on adding everything someone thinks of to the volume leader mass-market processor. Three related things are happening to help with this: 1) adding reconfigurable hardware to a general purpose processor or embedding the processor into reconfigurable hardware. Here we have Xilinx "Zynq" and MicroChip "PolarFire SoC" with ARM and RISC-V (respectively) hard CPU cores inside an FPGA. We also have Cypress PSoC which I believe is more like adding a small amount of reconfigurable hardware on the side of a conventional CPU core. If the performance needs of the general-purpose part of the processing are relatively low then you can use a "soft core" CPU built from the FPGA resources themselves. Each FPGA vendor has had their own custom instruction set and CPU core, but now people are moving more and more to instructions sets and cores they can use on any FPGA -- chiefly RISC-V. 2) making custom chips with a standard CPU core augmented with a few custom instructions / execution units. Again, much of this activity is centred around RISC-V though ARM has announced support for this with one or two of their standard CPU cores, initially the Cortex A35. Going into full production of a chip like this has costs in the low millions of dollars, with incremental unit costs as low as $1 to $10. Small numbers of custom chips (100+) can be made for $5 to $500 each depending on the size of the chip and the process node -- bigger, slower nodes are cheaper. 3) adding special purpose instructions that can be more flexibly applied to a larger range of problems. The main contender here is support for "Cray-style" processing of (possibly) long vectors of flexible length. If appropriately designed the same program can run at peak efficiency (for that chip) on CPUs with vastly different vector register lengths. This is in contrast to traditional SIMD where the program has to be rewritten every time a CPU is made with longer vector registers -- and it is very inconvenient to deal with data set sizes that are not a multiple of the vector length. If suitable primitives are included for predication of vector elements and divergent and convergent calculations then such a vector processor can run the same algorithms as GPUs (e.g. directly compiling CUDA and OpenCL to them). CPUs with sufficiently long vector registers can then compete directly in performance with GPUs on GPU-style code. All while staying tightly integrated with general purpose computations. ARM SVE and the RISC-V V extension are the examples of this, with I think the RISC-V version being the more flexible and forward-looking.

SiVal 6y ago

There's no doubt that we're getting an ever-widening variety of clients, but this "general purpose" notion is a bit slippery. I remember when "general purpose" meant that not only did computers crunch numbers for science and engineering, but they could finally handle more general business computing such as accounting and text-heavy business data processing, which was much larger than number crunching--more general purpose. Then we got microcomputers, and we added consumers and individual employees to "general purpose", and "word processing, word processing, word processing, and spreadsheets" became the new definition of general purpose computing, dwarfing big iron data processing. Then laser printers and desktop publishing, then Photoshop, CDs and "multimedia", and then The Internet (meaning the web) exploded and general purpose computing suddenly meant email, instant messaging, and so many new home pages that Yahoo could hardly list them all. In the Internet Boom, nobody defined general purpose computing as mostly word processing, much less business data processing, and forget about scientific calculating. Fast forward a couple more decades, and general purpose computing is streaming YouTube celebs, texting, binge watching streaming "TV", getting most of your news managed by Big Tech censors so you can't "misunderstand", keeping up with ever-streaming timelines of your 900 closest friends, going to work or school via Zoom, doing most of your shopping.... And where is word processing in this general purpose computing? Probably bigger than ever, but now buried in the "other" category because newer components of general purpose computing dwarf it. So if some of this is done with a laptop, and some with a "phone", and some with a flat screen in your car, and some with your TV remote, and some with an iPad...I suspect that "computers" could easily be described as either declining or growing for general purposes depending on which of many reasonable definitions you employed.

brucehoult 6y ago
You are trying to define tasks as "general purpose". They aren't and never can be. The point is that the basic modern computer design works well (enough) for almost all of those tasks. For the most part all that you need is the basic operations on 32 or 64 bit "integers": move, compare, add, subtract, and, or, xor, shift left, shift right. With this you can do spreadsheets, word processing, database, accounting, desktop publishing, web browsing, email, instant messaging, decoding MP3 audio and JPEG photos and MPEG video. And show it all on a GUI on a high resolution bitmapped display. You can increase the efficiency of some of those tasks with hardware and special purpose instructions for floating point arithmetic (though spreadsheets for example hardly benefit) and SIMD processing for media. See my other (top level) post. But you don't need them, especially if you have many general purpose CPU cores. Don't forget the original Macintosh did basically all of those tasks with just a simple integer-only CPU running at 8 MHz. No FPU, no GPU, no media instructions. A modern multi-GHz CPU can do all those tasks acceptably using only simple integer instructions as long as you don't insist on very high resolution video (and games) at high frame rates.

konschubert 6y ago

The economy is finally catching up with building use cases around what a computer can do. The result is that special-purpose computing is growing, and general-purpose computing is on the decline.

mathis 6y ago

Link was broken for me. This one works: https://dl.acm.org/doi/10.1145/3430936 https://dl.acm.org/doi/10.1145/3430936

JoeAltmaier 6y ago

"Decline" as in "Still growing but not as much as some other things". This analysis sounds like sour grapes. "Phones get all the love!" doesn't actually mean that general-purpose computing is declining. Just the opposite. It continues to grow in power as it always has.

tartoran 6y ago
I agree and the pool of tinkerers and intelectually curious has been growing steadily. There are so many free resources available that the barrier to entry has lowered enormously. However, the enormous growth has gone towards consumption and that is not necessarily a bad thing.

williesleg 6y ago

Part of The Great Reset. Computers are too powerful. Just give them a locked-down iPad. Of course, we'll have India write all the code, they do it cheap.

solinent 6y ago

I think as computers fail to get faster, we won't be able to improve performance without making application specific logical units, however, we may still be able to make it general purpose.

shadowgovt 6y ago
What I'm most interested in is what the trends will be in how those specialized circuits are developed. Circuit design has been marching steadily along for decades, and between the tools available to allow non-specialists to go from abstract hardware description to image-ready chip specification and the ever-cheapening, ever-broadening capabilities of custom chip fab, are we approaching an era where fabricating mid-range specialized circuits is on par with the difficulty of writing and compiling software, only a bit slower?
asdff 6y ago
I'm waiting for automatic code optimization. Write the code going from an input to an output however you like, show the computer your input and output, have it rewrite the most optimal (and probably very weird looking) code possible to go from your supplied input to your supplied output, and reap the efficiency rewards to mastering a stack of arcane low level code reference books without spending the thousands of hours of your precious and limited free time to do so. That would be a huge leap imo.

mannykannot 6y ago

The article is factually correct, but I feel that the analysis is missing some of the historical context. Here's a quite probably strained analogy to cosmology: Microprocessors and CMOS upended the computer industry, to the extent that a few years later, all the big companies in computing, up to that point, were in precipitous decline (even IBM, which embraced the new world order with the PC, only delayed this reckoning.) In those days, microprocessors and DRAM alone were the cutting edge of technology, and they opened up all sorts of possibilities (though this also depended on some additional special-purpose equipment, notably for graphics and networking.) One might draw an analogy here to the Big Bang. What happened next was like a period of cosmic inflation, in which Moore's law and Dennard scaling (plus fiber optics and the wiring of the world) created exponential growth. As in cosmology, we end up with a much bigger but quite uniform universe, in this case because the growth of the basic technology alone was enough fuel for all the innovation we could come up with. It was only after inflation that the universe became really interesting. It is still overwhelmingly hydrogen, but it has differentiated: there are also galaxies, stars, planets and people. So, the long-delayed conclusion to this analogy: innovation in computing is now more mature, but it is not shrinking, it has diversified - and there are still 10^n (for some large n) 8- and 16-bit microprocessors being made, and there are still hobbyists doing clever things with what is now basic, even primitive, hardware (that, not so long ago, was unattainable) - but we also have emerging technologies (machine translation and autonomous vehicles, for example) that quite a few people, not so long ago, assumed would be forever beyond the capabilities of mere machines.

GLGirty 6y ago
A had a similar reaction to the article, and I appreciate your cosmic analogy. The metaphor that came to my mind was the long persistence of steam power after electric motors were invented. Initially, 'going electric' meant replacing your one giant steam boiler with one giant electric motor, and doing everything else the same way. The factory remained organized around the drive shafts and pulleys and mechanical distribution systems. There was little advantage. It took decades of experimentation to gain the insights of how many small motors and task lighting could allow a factory to be optimized for task flow, not power distribution. And then it took longer for those insights to diffuse through slow human networks. Hardware has developed so fast that software had no time or incentive to mature. Each decade's tech just gets ossified into the stack because hardware was making the stack faster at a better rate than human insight. Every few decades, we get lucky and an invention happens when the ecosystem can use it... so we get compilers and sql and automated tests. But then other insights like immutable data structs just stay niche. I hope that as hardware progress flattens, opportunities emerge for better software paradigms. Maybe this will coincide with the craft of software becoming introspective about it's myriad social issues.

kristianpaul 6y ago

Yeah computers are everywhere now “ Collectively, these findings make it clear that the economics of processors has changed dramatically, pushing computing into specialized domains that are largely distinct and will provide fewer benefits to each other. Moreover, because this cycle is self-reinforcing, it will perpetuate itself, further fragmenting general purpose computing. As a result, more applications will split off and the rate of improvement of universal processors will further slow.”

Zigurd 6y ago

"Personal" computers were subversive when first introduced. They were designed for complete control by an individual. They were also, initially, not networked. When networks first appeared it was with the assumption that the user community of the network did not have a persistent high level of malicious security threats. In a kludgy, gradual way personal computers were tamed. This was inevitable. If you network completely general purpose, user-controlled machines in an institutional environment you will get security chaos. Some of this, though, like putting digital rights management into personal computers, was tragically evil. What we are left with is neither here nor there: Computers are not secure. Our communications are not secure and are under constant surveillance. And we do not control machines we supposedly own. Lose, lose, lose. You cannot have security and surveillance and central control of "personal" devices. You cannot protect "digital rights" and sell entertainment content to users that truly control their personal computers.

mattkevan 6y ago

Thing is, in the ‘olden days’, the only thing you could do with a home computer was tinker - the job of a computer was to be a computer but little else. And the number of people interested in doing that was small. Today, the job of a computer is everything. Computers are in everything and do everything. They are our interface to the world. Their value is what they enable - writing, tax returns, video consumption, gaming and everything else, not the fact they’re a ‘computer’. For those of us who want a computer to tinker with, we’ve never had it so good. There’s so many more options than there’s ever been for hackable tech. Just because most people don’t want to do that doesn’t mean they’re wrong, it just means they have different priorities. I love the fact I don’t have to muck about with my iPhone to get it to work just as much as I love mucking about with my raspberry pi to get it to work.

deleted 6y ago
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Terretta 6y ago
Look at cars, consider both tinkering and “right to repair, but with no guarantee on know-how”.
higerordermap 6y ago
But most tinkering journeys do not start with dedicated hardware. They start with hardware intended for something else, generally on a quest to solve some problem or do something cool..
- shadowgovt 6y ago
  The evolution of graphics cards into CUDA machines for machine learning applications is one example of this phenomenon.
wayneftw 6y ago
> I love the fact I don’t have to muck about with my iPhone to get it to work... Imagine being able to quickly and easily add your own custom programs to your iPhone without asking anyone's permission though? Also, what if - as the owner of that device - you had full control over it's capabilities like you do with a desktop computer? Adding these abilities certainly would not be the downfall of phone ecosystems as we know them. People that want convenience wouldn't leave their app store. The fact that they are our interface to the world is exactly the reason why we need to take control of them away from the Apple/Google oligarchy. People need to stop comparing these devices to gaming consoles, which don't impact the world even a fraction as much as smartphones, when we're talking about making new rules for running smartphone platforms.

ip26 6y ago

This fragmentation means that parts of computing will progress at different rates. This will be fine for applications that move in the 'fast lane,' where improvements continue to be rapid, but bad for applications that no longer get to benefit from field-leaders pushing computing forward, and are thus consigned to a 'slow lane' of computing improvements. This transition may also slow the overall pace of computer improvement, jeopardizing this important source of economic prosperity. This is perceptive, but also red ocean thinking. Moving ML to GPU is only bad for other workloads if CPU languishes as a result. But I think there's reason to believe it's a blue ocean. The more performance ML gets out of GPU, the more money the ML business can pour into semiconductors, batteries, EDA, and so forth. CPU benefits from all of these. I think you can see this at work in the laptop market. Cell phone R&D has driven battery technology. Server R&D has driven efficient high-performance x86 cores. Both have driven power-efficient process nodes. Put them together and you can make incredible laptops.

carapace 6y ago

To some extent this is just the Futumura Projections writ large, eh? ( https://en.wikipedia.org/wiki/Partial_evaluation#Futamura_projections https://en.wikipedia.org/wiki/Partial_evaluation#Futamura_pr... ) Anyway, I don't see the problem. I don't see how e.g. better GPUs cannibalize the performance of general-purpose CPUs, and I doubt that the market for general purpose computing isn't already saturated. You can buy CPUs for a couple of bucks (little ones), you have a supercomputer in your pocket (phone), and people only buy fancy chips to play video games.

jackcosgrove 6y ago

I see a connection between this and the troubles Intel is facing, and the general decline of CISC processors.

ksec 6y ago

This is a very hardware focused overview of technology. To summarise, Moore's Law is dead. Specialise processors takes over. And they will be owned by specific Companies. That is all true as I have been posting pretty much exactly the same on HN for a few years. The real question is though, does it matter? I am not convinced, at least not yet convinced what we are doing now on a computer is that much different to 20 years ago. Excel, Web Browsing, Media Consumption and Content Creation like Photoshop. The only real recent innovation is Machine Learning that greatly increase productivity in certain niche. And it does not seems to me any of the above activity is going to fundamentally shift. There is nothing in terms of Hardware tech limitation that is holding up performance for further productivity increase, rather it is Software that is not getting much improvement if you consider the worst and best software could have performance different of 100x. To give an example, I could bet with few billion dollar we could create a Hardware GPU that is 80% close to Nvidia GPU or AMD GPU. I am also willing to bet even with 10 Billion dollar funding we cant get a CUDA Compatible Library or Drivers that is 80% of what Nvidia is offering today.

johnnycerberus 6y ago

I really believe that distributed systems will weight more in the future and that virtual machines will be able to abstract both local resources and remote ones and new paradigms will be created in order to program these. We are still in the early days of programming and there's a lot of room for innovation.

magwa101 6y ago

It's a strange article. Portable languages have rendered "general purpose CPUs" moot and allowed for incredible innovation at the chip level. Some may recall that making software run on different chips was a huge deal. Now, you use a portable language and potentially portable APIs that are specific to the purpose at hand. The article ignores that vast access to general capabilities are now available through the internet. With an even higher abstraction that again renders the underlying CPU type irrelevant. Who knows or cares what CPU is serving this page.