12 ms·

Analog computing may be coming back

99 points by mbellotti 4y ago

choudharism 4y ago
The mark of a maturing domain is the evolution from only general tools to general + specialized. We've gone from only CPUs to CPU + GPU to specialized AI chips (Neural Engine, Tensor chips etc.) and specialized computing is a big tent which can fit many different architectures together. Analog computing is the closest thing to bioengineering in fundamental computer science that I know of, so I am confident that it will find a niche. I remember reading about Mythic AI here on HN, who were doing some cool work with analog computing chips for ML. My hunch is that matrix multiplication is the most expensive mathematical operation we do as a society (not unit expensive, but in overall absolute cost) - and our progress in AI is directly proportional to how easy / cheap it is to run.
- noduerme 4y ago
  Maybe I'm missing something, but wouldn't any optical computer have to still funnel signal through binary logic gates at some point? In what sense is that any more analog than (digital recordings on analog) magnetic tape decoded by a modem? The ultimate computation is still 1/0
  - chongli 4y ago
    You can do math with analog circuits. This was the original purpose of the opamp (operational amplifier) [1]. [1] https://en.wikipedia.org/wiki/Operational_amplifier https://en.wikipedia.org/wiki/Operational_amplifier
    - noduerme 4y ago
      huh. So the distinction between analog and digital has nothing to do with whether the logic itself is binary, just whether the delivery of signal to gate is absolute or ranged? Something has to gate the signal, right? I always thought "analog" referred to processes that didn't reduce things to a binary at some step along the way...(?)
      - nextaccountic 4y ago
        opamps aren't binary
      - chongli 4y ago
        As far as the circuits are concerned, there’s no such thing as digital. For human engineers, digital is a convention. Well, technically, there are numerous digital logic conventions based on different voltage standards. For example, you might decide that 0 volts is a logical 0 and 5 volts is a logical 1. If you get everyone to agree to this convention then you can build components that talk to each other. Unfortunately, it’s very difficult (impossible) to get to exactly 0 or exactly 5 volts. So instead you decide that anything less than 2 volts is a logical 0 and anything greater than 3 volts is a logical 1. This setup makes your circuits quite robust to noise. To further improve things, you might decide that when you want to output a logical 0 you must produce a voltage less than 1 volt and if you want to output a logical 1 you must produce a voltage above 4 volts. This convention allows your system to continually correct voltages away from the undefined region (between 2 and 3 volts). A marginal input of 3.1 volts gets interpreted as a logical 1 and then output above 4 volts. This “self-correction” is what made digital computers the revolution they are.
        noduerme 4y ago
        so .. the only thing I could imagine making a system "analog" would be if each voltage were treated differently, rather than segregated into 0 or 1 as you just described. If a whole range of signal between 0 and 5 volts were directly output to something like a speaker system, that would be analog. I guess I'm wondering how this optical computer would get around the bottleneck of reducing everything to binary as you described with an electrical system.
        alar44 4y ago
        You'd use an ADC.
  - sbaiddn 4y ago
    Get a ruler. Draw a line 10 cm long, AB. Now grab end B and draw another another line, BC, with a certain angle $/alpha$ wrt to the first. Now measure the distance AC. Congratulations! You have just built an analog computer to use the Law of Cosines to solve for line segment AC. Non-dimensionalize your result and its a general LofC solver. A problem that (I suspect) would take the majority of modern day Eng undergrads a week to program without the use of the math lib[1], can be solved by any keen middle schooler. Now build a robot that measures AC for you and you have an API for your analog computer. Typically an analog computer is thought of as a set of opamps and diodes, whose currents and voltages solve a set of non-linear ODEs; but thats a very narrow view. An analog computer is, ultimately, any physics experiment whose model is known Wind tunnel? Navier Stokes analog computer Cold atoms traveling through a double slit in a magnetic field? Analog Quantum computer RCL circuit? Analog computer solving the response of a car's suspension. [1] code reuse and libraries are a big reason why digital computers are more popular to solve models nowadays. Cost, bandwidth, are another. Ostensibly so is reproducibility. But if CS scientists cannot get reproducible builds, what hope does a humble physicist hacking on C or Matlab have?
    - mellavora 4y ago
      > Typically an analog computer is thought of as a set of opamps and diodes, whose currents and voltages solve a set of non-linear ODEs; Oh oh oh! and also resistor/capacitor circuits! Great for integration or differentiation! Integration and differentiation have interesting audio effects; maybe our ears might be one of the better analog computers.
      - fjkdlsjflkds 4y ago
        > Integration and differentiation have interesting audio effects An integrator is just a low-pass filter and a differentiator is just a high-pass filter, so you'll most likely get "boring" audio effects ;)
      - sbaiddn 4y ago
        Our senses are incredibly good.
    - 4y ago
  - dahart 4y ago
    Digitizing the output of an analog computation doesn’t make it a digital computation. It’s still analog. The ultimate computation is not binary 1/0, it gets converted to binary after the computation. We may be able to save time or energy by changing representation. Imagine a NxN matrix-matrix multiply. The computation part is (naively) N^3 multiplies. The conversion back to digital is only N^2 operations, and those operations may be much simpler than digital multipliers. If there’s a way to do the N^3 multiplies as analog, then we can potentially save a lot by converting to and from binary to enable the analog phase.
- deleted 4y ago
  [deleted]
orbifold 4y ago
One of the things that I fully not expect to be successful is optical computing. There are just a lot of academic groups that are doing optics and they like to invent new reasons why whatever they are up to is relevant. For physics reasons the integration density of optical compute elements is abysmal and will remain so forever. Other technologies like spintronics at least have the chance to work sometime in the future. There were projects on wafer scale optical computing already in the 80-90s at MIT Lincoln labs, so this isn't exactly a new idea either. We have a new group at our institute doing "Neuromorphic Quantum Photonics", they publish in high-impact glossy journals, doesn't change that it is in my opinion mostly hype and bullshit.
- tecleandor 4y ago
  I friend I had researching in optical computing around 5 years ago always said "Nah, not even near". Not that ain't gonna happen, but only basic interfaces seemed feasible at the time. (But I'm not familiar with the field and I could be wrong or not remember exact details)
  - orbifold 4y ago
    More power to them if it works, you can do cool things with it for sure. I am continuously amazed what you can do in quantum optics etc. but it isn't anywhere near to practical except for optical communication.
- staunton 4y ago
  > For physics reasons the integration density of optical compute elements is abysmal and will remain so forever. Could you give some details? Claims about "forever" often don't hold up. I guess you're referring to things like component size in relation to the wavelength of light used? One could use smaller wavelengths. Integrated photonics is certainly being done and also commercially relevant (in telecommunications). What integration density would you consider not-abysmal? How much does integration density matter if you have very low loss (which means low power dissipation, a huge problem for semiconductor electronics) and can just make big chips? There is also research arguing that optoelectronics might eventually be very useful for computing, e.g. recently [1]. (Yes, this is by researchers who need to appear relevant. However, if we dismiss their arguments based on that alone, we can abolish all research altogether.) Why do you disagree? Again, you were talking about forever. [1]: https://www.nature.com/articles/s41467-022-29252-1 https://www.nature.com/articles/s41467-022-29252-1
jojobas 4y ago
Probably the best 20 minutes you can spend if you haven't really heard of analog computers. https://www.youtube.com/watch?v=IgF3OX8nT0w https://www.youtube.com/watch?v=IgF3OX8nT0w
- bil7 4y ago
  gotta love Veritasium. Every video feels like time well spent.
someonewhocar3s 4y ago
[dead]
asicsp 4y ago
See also https://semiengineering.com/can-analog-make-a-comeback/ https://semiengineering.com/can-analog-make-a-comeback/ Discussion: https://news.ycombinator.com/item?id=32106546 https://news.ycombinator.com/item?id=32106546 (130 points | 6 months ago | 125 comments)
giladvdn 4y ago
The absurdity of suggesting optical computing is a good pathway to efficiency is that our brains efficiently use electrons and are doing just fine.
- nathan_compton 4y ago
  Our brains are subject to very different design constraints. Wheels are very efficient, but nature doesn't use them because the environment and the exigencies of biological reproduction and repair indicate other strategies. It really isn't a simple thing.
  - giladvdn 4y ago
    Interesting analogy, and I see your point. What I'm trying to say is, we know that there's way to perform certain computations that's orders of magnitude more efficient than we've ever achieved. We have a working example of it. And yet, we choose to develop a wholly different technology, from scratch, that's unproven, instead of trying to emulate or understand what already have.
    - pjerem 4y ago
      We are far from having a model of how the brain really works. Furthermore, i know it's a really common analogy but the brain is not really comparable to a computer. The brain is not programmable, it only have a single function which is "given x input, what output is more likely to keep the organism alive and well". It's a complex task, for sure. But that's not a computer. A computer is a machine on which you can run arbitrary programs. I can't plug some wires in your brain and program it to do what I want. It's not that the sockets for my wires are missing, it's just that the physical structure doesn't allow generic computing. If you really wanted to make an analogy, you could say that the brain is an electric circuit. You cannot program an electric circuit. It does what it's wired to do. You can't say that your electrical circuit is faster than a computer. It makes no sense. So it's a false assumption to say that brains are faster than computers because it's just something that you cannot compare.
amatic 4y ago
Another interpretation of the name "analog computers" is that they compute by analogy - by simulating the problem in a different medium. When analog computers were created, the there were no digital computers to make the distinction between analog (continuous) vs digital (discrete). There were mechanical computers that used gears and shafts, and angular speed as the main variable; electronic computers that used voltages, there is a famous hydraulic computer that used water levels and rates of flow to represent variables, etc. You start by examining your problem in mathematical terms and write down the differential equations that describe it. For example, you have a model of a car suspension with parameters for spring stiffness, damping etc. You put the model into the computer, and play with the parameters to see how things work. Not that different from modern simulations. The one advantage over modern simulations is that you might get a better "feel" for the system - or so the proponents of the analog used to say, before digital computers replaced them.
singularity2001 4y ago
Analog will likely come back but for other reasons: Neural networks don't require precise calculations and Hintons forward forward networks put into hardware would be several orders of magnitudes more efficient, even without photons. "AI inferencing is heavily dependent on multiply/accumulate operations, which are highly efficient in analog." If you know of any startup working on this let me know because I'd love to join the revolution.
- frafra 4y ago
  Almost one year ago, Veritasium interviewed https://mythic.ai/ https://mythic.ai/ in one of his videos on YouTube: https://youtu.be/GVsUOuSjvcg?t=898 https://youtu.be/GVsUOuSjvcg?t=898.
  - Drunk_Engineer 4y ago
    They ran out of money last year.
- denton-scratch 4y ago
  Neural nets, AFAIAA, usually rely on the digital simulation of analog processes. NN weights, inputs and outputs are all continuous values. I suspect an integrated all-analog NN chip will be along soon, and might run more efficiently and faster than the digital simulation. I don't see why it shouldn't use photonics.
- brianderson 4y ago
  Coincidentally, I recently posted a collection of neuromorphic job openings to LinkedIn: https://www.linkedin.com/posts/brian-anderson-6739ba25_neuromorphic-machinelearning-jobs-activity-7025813029011300352-sClP https://www.linkedin.com/posts/brian-anderson-6739ba25_neuro... My team at Intel Labs is hiring, as are a number of well-funded startups.
zozbot234 4y ago
Analog computing sucks. It's inherently sensitive to noise and random variation in your devices that's basically ubiquitous with modern chip-making processes. Digital electronics actively reject noise at every step in a computation, albeit at the cost of wasting energy in the process. With analog, a more complex computation becomes exponentially harder.
- silvestrov 4y ago
  Analog computers are like LP records: only good for nostalgia.
  - nanna 4y ago
    Depth of sound, long term storage, ease of navigation, there are lots of reasons people prefer vinyl that aren't based in nostalgia.
    - foobarbecue 4y ago
      Depth? You mean like a large range in available amplitudes (loud vs soft)?
      - alar44 4y ago
        Probably, and they're wrong.
    - BitwiseFool 4y ago
      No DRM and the fact that you actually own it is a big plus.
      - tinus_hn 4y ago
        Like a CD?
        foobarbecue 4y ago
shrubble 4y ago
I think that the author is wrong. Analog computer's weak point is the power supply, such that many companies making them ended up having to manufacture their own to very high standards, such as big capacitors with 0.1% tolerance. Reason is that you are using the analog voltage and thus poor power regulation leads to inaccurate results. With newer analog computer setups more is integrated into the chip itself, making power supply issues much less of a problem.
- yummypaint 4y ago
  On the other hand, making large passives on silicon is extremely challenging to do accurately, and relatively expensive in terms of area. Most power regulators rely on external caps for this reason.
Aldipower 4y ago
This topic cannot miss a post mentioning Bern Ulmann. Here a video, where's he demonstration the Juksowski profile. https://www.youtube.com/watch?v=nP84Dv01y4A https://www.youtube.com/watch?v=nP84Dv01y4A
gjvc 4y ago
paraffin lamps may be coming back, too
MichaelMoser123 4y ago
isn't a quantum computer a kind of analog computer? Wikipedia says "An analog computer or analogue computer is a type of computer that uses the continuous variation aspect of physical phenomena" https://en.wikipedia.org/wiki/Analog_computer https://en.wikipedia.org/wiki/Analog_computer
- red75prime 4y ago
  Quantum logic gates perform discrete operations on quantum states of qubits. That is there's a finite number of quantum states that qubits can end up in (in the ideal quantum computer).
ratrocket 4y ago
https://archive.ph/DeK8H https://archive.ph/DeK8H
xor99 4y ago
Analog computers can perform matmul operations without data movement using physical properties such as conductance changes in a very small volume. If noise and random variation can be modelled successfully then in certain cases they are obviously going to be better (e.g. energy use in edge applications). The discussion is not specific enough to applications to be useful. AI data centres are not going to be using this stuff anytime soon for example. On the other hand, you do not want an NVIDIA GPU inserted into your body.
jerf 4y ago
It seems to me that punters overly excited to declare that everything we've ever done up to this point is wrong and there's a new paradigm coming that will obsolete everything, as well as people being critical of the new thing, tend to miss a very important aspect of digital computation that will prevent analog computation from ever simply "taking over": Digital computation is essentially infinite composable. It does not matter how many gates you throw your 1s and 0s through; they will remain ones and zeros. While floating point numerical computations carry challenges, they are at least deterministic challenges. You can build things like an SHA512 hash which can ingest gigabytes upon gigabytes, with the entire computation critically dependent at every step upon all previous computations in the process, a cascading factor of literally billions and billions, and deterministically get the exact same SHA512 hash for the exact same input every time. This property is so reliable that we don't even think about it. Analog computers can not do that. You could never build a hash function like that out of analog parts. You can not take the output of an analog computer and feed it back into the input of another analog computation, and then do it billions upon billions of times, and get a reliable result. Such a device would simply be a machine for generating noise. Analog computing fits into the computing paradigm as another "expansion card". It may take isolated computations and perform them more efficiently. Perhaps even important computations. But they will always be enmeshed in some digital computer paradigm. Breathless reports about how they're "coming back" and coming soon and taking over are just nonsense. (I speak generally, this walled-off article may or may not have made such claims, I dunno.) So many things about how digital computers work that you just take for granted are simply impossible for analog computers, structurally; something as simple as taking a compressed representation of a starting state for your analog computer is something you need a digital computer for, because our best compression algorithms have the same deep data dependencies that I mentioned for the hashing case. Useful, interesting, innovative, gonna make some people some money and create some jobs? Sure. Something we should all go gaga over? No more than a new database coming out. It's going to be a tool, not a paradigm shift.
jefurii 4y ago
Analog computing has been used by musicians for awhile now. Synthesizers are basically analog computers. Bob Moog was an engineer whose genius was figuring out how to connect keyboards to lab equipment and how to hide enough of the guts to make the gear approachable to musicians. West Coast synthesists like Buchla took the opposite approach of appreciating the sound of analog computing for what it is. Synthesizers tried to hide it for awhile behind layers of user interface, but especially with the Eurorack boom of the last decade or so you can really see that synthesizers are simply specialized analog computers. Lots of synth modules openly use the same terminology as analog computing: filters, amplifiers, multipliers, low-pass gate, sample and hold, sequencer, etc. Musicians like Hainbach use actual test equipment in their music. Guitar effect rigs are also basically analog computers. They're just not used for numerical computation. update: The Signal State is a Zach-like game where you solve puzzles by programming analog computers; in was inspired by Eurorack synthesizers.
- alar44 4y ago
  No, synthesizers and guitar pedals are as much computers as a trumpet. "It translates the lip movement through the horn and amplifies the sound, thusly it's multiplying and therefore a computer!" That is stretching the definition of a computer to absurdity, sorry.
lambdaxymox 4y ago
It's interesting that the black art of analog design never really goes away even from computing. I was reading Geoffrey Hinton's "Mortal Computation" where he briefly speculated towards the end on low power (in watts used, not capability) neural networks being embedded in hardware via something like memristor networks. It makes me imagine neural networks being distributed as a template (i.e. a blob of XML describing the topology and the weights) and then the network weights get tuned a little different to each device or device model to get the best performance per watt out of them. Since every device is physically a little bit different from the next, in precision analog applications with discrete components the components have to be matched. This is often the case in assembling differential pairs in analog audio circuits. Similarly with the control system parameters with hard drives. Since each DC motor is a little bit different from the next, the control loop gets tuned at the factory to each drive. So in this case, the neural network gets matched to the circuit it's embedded in. Mortal computation indeed, each neural network becomes truly unique. I could be full of it, but it's fun to imagine at least.
rasz 4y ago
From Travis Blalock (first real optical mouse) Oral History: "each array element had nearest neighbor connectivity so you would calculate nine correlations, an autocorrelation and eight cross-correlations, with each of your eight nearest neighbors, the diagonals and the perpendicular, and then you could interpolate in correlation space where the best fit was. " "And the reason we did difference squared instead of multiplication is because in the analog domain I could implement a difference-squared circuit with six transistors and so I was like “Okay, six transistors. I can’t do multiplication that cheaply so sold, difference squared, that’s how we’re going to do it.” "little chip running in the 0.8 micron CMOS could do the equivalent operations per second to 1-1/2 giga operations per second and it was doing this for under 200 milliwatts, nothing you could have approached at that time in the digital domain." Avago H2000 chip did all the heavy lifting _in analog domain_. No DSP, it was too expensive for digital domain (cost of first civilian handheld GPS receivers also doing heavy autocorrelation, 1998 Garmin StreetPilot was $400-550 retail).
ghoul2 4y ago
As I understand it, analog computing is entirely impractical simply from a Information Theoretic viewpoint. For a signal to convey 8-bits worth of information, it will need to have 256 distinct levels. It we want the signal to range from, lets say, 0 to 5v (which is already quite high), each level only has about 2mV range. This much can easily come from cross-talk, EMI and power supply noise. So all your logic/calculation will be wrong. Once you start talking about 16 bits, it becomes entirely ridiculous: we now can only have 75uV range for each level. This is getting into RF interference territory - just receiving a phone call close to such an analog signal would disrupt it. The way I understand it, there is simply not enough SNR available in our electronics (on die traces or PCB traces) for analog computing to work. Thats why we restrict the number of level we use in our signal: digital being just two level, but even with higher level-counts, we typically use 4 levels or 8. This is somewhat analog, but not really. I am not an EE, so I am entirely open to being corrected on this.

99 points by mbellotti 4y ago

The mark of a maturing domain is the evolution from only general tools to general + specialized. We've gone from only CPUs to CPU + GPU to specialized AI chips (Neural Engine, Tensor chips etc.) and specialized computing is a big tent which can fit many different architectures together. Analog computing is the closest thing to bioengineering in fundamental computer science that I know of, so I am confident that it will find a niche. I remember reading about Mythic AI here on HN, who were doing some cool work with analog computing chips for ML. My hunch is that matrix multiplication is the most expensive mathematical operation we do as a society (not unit expensive, but in overall absolute cost) - and our progress in AI is directly proportional to how easy / cheap it is to run.

noduerme 4y ago
Maybe I'm missing something, but wouldn't any optical computer have to still funnel signal through binary logic gates at some point? In what sense is that any more analog than (digital recordings on analog) magnetic tape decoded by a modem? The ultimate computation is still 1/0
- chongli 4y ago
  You can do math with analog circuits. This was the original purpose of the opamp (operational amplifier) [1]. [1] https://en.wikipedia.org/wiki/Operational_amplifier https://en.wikipedia.org/wiki/Operational_amplifier
  - noduerme 4y ago
    huh. So the distinction between analog and digital has nothing to do with whether the logic itself is binary, just whether the delivery of signal to gate is absolute or ranged? Something has to gate the signal, right? I always thought "analog" referred to processes that didn't reduce things to a binary at some step along the way...(?)
    - nextaccountic 4y ago
      opamps aren't binary
    - chongli 4y ago
      As far as the circuits are concerned, there’s no such thing as digital. For human engineers, digital is a convention. Well, technically, there are numerous digital logic conventions based on different voltage standards. For example, you might decide that 0 volts is a logical 0 and 5 volts is a logical 1. If you get everyone to agree to this convention then you can build components that talk to each other. Unfortunately, it’s very difficult (impossible) to get to exactly 0 or exactly 5 volts. So instead you decide that anything less than 2 volts is a logical 0 and anything greater than 3 volts is a logical 1. This setup makes your circuits quite robust to noise. To further improve things, you might decide that when you want to output a logical 0 you must produce a voltage less than 1 volt and if you want to output a logical 1 you must produce a voltage above 4 volts. This convention allows your system to continually correct voltages away from the undefined region (between 2 and 3 volts). A marginal input of 3.1 volts gets interpreted as a logical 1 and then output above 4 volts. This “self-correction” is what made digital computers the revolution they are.
      - noduerme 4y ago
        so .. the only thing I could imagine making a system "analog" would be if each voltage were treated differently, rather than segregated into 0 or 1 as you just described. If a whole range of signal between 0 and 5 volts were directly output to something like a speaker system, that would be analog. I guess I'm wondering how this optical computer would get around the bottleneck of reducing everything to binary as you described with an electrical system.
        alar44 4y ago
        You'd use an ADC.
- sbaiddn 4y ago
  Get a ruler. Draw a line 10 cm long, AB. Now grab end B and draw another another line, BC, with a certain angle $/alpha$ wrt to the first. Now measure the distance AC. Congratulations! You have just built an analog computer to use the Law of Cosines to solve for line segment AC. Non-dimensionalize your result and its a general LofC solver. A problem that (I suspect) would take the majority of modern day Eng undergrads a week to program without the use of the math lib[1], can be solved by any keen middle schooler. Now build a robot that measures AC for you and you have an API for your analog computer. Typically an analog computer is thought of as a set of opamps and diodes, whose currents and voltages solve a set of non-linear ODEs; but thats a very narrow view. An analog computer is, ultimately, any physics experiment whose model is known Wind tunnel? Navier Stokes analog computer Cold atoms traveling through a double slit in a magnetic field? Analog Quantum computer RCL circuit? Analog computer solving the response of a car's suspension. [1] code reuse and libraries are a big reason why digital computers are more popular to solve models nowadays. Cost, bandwidth, are another. Ostensibly so is reproducibility. But if CS scientists cannot get reproducible builds, what hope does a humble physicist hacking on C or Matlab have?
  - mellavora 4y ago
    > Typically an analog computer is thought of as a set of opamps and diodes, whose currents and voltages solve a set of non-linear ODEs; Oh oh oh! and also resistor/capacitor circuits! Great for integration or differentiation! Integration and differentiation have interesting audio effects; maybe our ears might be one of the better analog computers.
    - fjkdlsjflkds 4y ago
      > Integration and differentiation have interesting audio effects An integrator is just a low-pass filter and a differentiator is just a high-pass filter, so you'll most likely get "boring" audio effects ;)
    - sbaiddn 4y ago
      Our senses are incredibly good.
  - 4y ago
- dahart 4y ago
  Digitizing the output of an analog computation doesn’t make it a digital computation. It’s still analog. The ultimate computation is not binary 1/0, it gets converted to binary after the computation. We may be able to save time or energy by changing representation. Imagine a NxN matrix-matrix multiply. The computation part is (naively) N^3 multiplies. The conversion back to digital is only N^2 operations, and those operations may be much simpler than digital multipliers. If there’s a way to do the N^3 multiplies as analog, then we can potentially save a lot by converting to and from binary to enable the analog phase.
deleted 4y ago
[deleted]

orbifold 4y ago

One of the things that I fully not expect to be successful is optical computing. There are just a lot of academic groups that are doing optics and they like to invent new reasons why whatever they are up to is relevant. For physics reasons the integration density of optical compute elements is abysmal and will remain so forever. Other technologies like spintronics at least have the chance to work sometime in the future. There were projects on wafer scale optical computing already in the 80-90s at MIT Lincoln labs, so this isn't exactly a new idea either. We have a new group at our institute doing "Neuromorphic Quantum Photonics", they publish in high-impact glossy journals, doesn't change that it is in my opinion mostly hype and bullshit.

tecleandor 4y ago
I friend I had researching in optical computing around 5 years ago always said "Nah, not even near". Not that ain't gonna happen, but only basic interfaces seemed feasible at the time. (But I'm not familiar with the field and I could be wrong or not remember exact details)
- orbifold 4y ago
  More power to them if it works, you can do cool things with it for sure. I am continuously amazed what you can do in quantum optics etc. but it isn't anywhere near to practical except for optical communication.
staunton 4y ago
> For physics reasons the integration density of optical compute elements is abysmal and will remain so forever. Could you give some details? Claims about "forever" often don't hold up. I guess you're referring to things like component size in relation to the wavelength of light used? One could use smaller wavelengths. Integrated photonics is certainly being done and also commercially relevant (in telecommunications). What integration density would you consider not-abysmal? How much does integration density matter if you have very low loss (which means low power dissipation, a huge problem for semiconductor electronics) and can just make big chips? There is also research arguing that optoelectronics might eventually be very useful for computing, e.g. recently [1]. (Yes, this is by researchers who need to appear relevant. However, if we dismiss their arguments based on that alone, we can abolish all research altogether.) Why do you disagree? Again, you were talking about forever. [1]: https://www.nature.com/articles/s41467-022-29252-1 https://www.nature.com/articles/s41467-022-29252-1

jojobas 4y ago

Probably the best 20 minutes you can spend if you haven't really heard of analog computers. https://www.youtube.com/watch?v=IgF3OX8nT0w https://www.youtube.com/watch?v=IgF3OX8nT0w

bil7 4y ago
gotta love Veritasium. Every video feels like time well spent.

someonewhocar3s 4y ago

[dead]

asicsp 4y ago

See also https://semiengineering.com/can-analog-make-a-comeback/ https://semiengineering.com/can-analog-make-a-comeback/ Discussion: https://news.ycombinator.com/item?id=32106546 https://news.ycombinator.com/item?id=32106546 (130 points | 6 months ago | 125 comments)

giladvdn 4y ago

The absurdity of suggesting optical computing is a good pathway to efficiency is that our brains efficiently use electrons and are doing just fine.

nathan_compton 4y ago
Our brains are subject to very different design constraints. Wheels are very efficient, but nature doesn't use them because the environment and the exigencies of biological reproduction and repair indicate other strategies. It really isn't a simple thing.
- giladvdn 4y ago
  Interesting analogy, and I see your point. What I'm trying to say is, we know that there's way to perform certain computations that's orders of magnitude more efficient than we've ever achieved. We have a working example of it. And yet, we choose to develop a wholly different technology, from scratch, that's unproven, instead of trying to emulate or understand what already have.
  - pjerem 4y ago
    We are far from having a model of how the brain really works. Furthermore, i know it's a really common analogy but the brain is not really comparable to a computer. The brain is not programmable, it only have a single function which is "given x input, what output is more likely to keep the organism alive and well". It's a complex task, for sure. But that's not a computer. A computer is a machine on which you can run arbitrary programs. I can't plug some wires in your brain and program it to do what I want. It's not that the sockets for my wires are missing, it's just that the physical structure doesn't allow generic computing. If you really wanted to make an analogy, you could say that the brain is an electric circuit. You cannot program an electric circuit. It does what it's wired to do. You can't say that your electrical circuit is faster than a computer. It makes no sense. So it's a false assumption to say that brains are faster than computers because it's just something that you cannot compare.

amatic 4y ago

Another interpretation of the name "analog computers" is that they compute by analogy - by simulating the problem in a different medium. When analog computers were created, the there were no digital computers to make the distinction between analog (continuous) vs digital (discrete). There were mechanical computers that used gears and shafts, and angular speed as the main variable; electronic computers that used voltages, there is a famous hydraulic computer that used water levels and rates of flow to represent variables, etc. You start by examining your problem in mathematical terms and write down the differential equations that describe it. For example, you have a model of a car suspension with parameters for spring stiffness, damping etc. You put the model into the computer, and play with the parameters to see how things work. Not that different from modern simulations. The one advantage over modern simulations is that you might get a better "feel" for the system - or so the proponents of the analog used to say, before digital computers replaced them.

singularity2001 4y ago

Analog will likely come back but for other reasons: Neural networks don't require precise calculations and Hintons forward forward networks put into hardware would be several orders of magnitudes more efficient, even without photons. "AI inferencing is heavily dependent on multiply/accumulate operations, which are highly efficient in analog." If you know of any startup working on this let me know because I'd love to join the revolution.

frafra 4y ago
Almost one year ago, Veritasium interviewed https://mythic.ai/ https://mythic.ai/ in one of his videos on YouTube: https://youtu.be/GVsUOuSjvcg?t=898 https://youtu.be/GVsUOuSjvcg?t=898.
- Drunk_Engineer 4y ago
  They ran out of money last year.
denton-scratch 4y ago
Neural nets, AFAIAA, usually rely on the digital simulation of analog processes. NN weights, inputs and outputs are all continuous values. I suspect an integrated all-analog NN chip will be along soon, and might run more efficiently and faster than the digital simulation. I don't see why it shouldn't use photonics.
brianderson 4y ago
Coincidentally, I recently posted a collection of neuromorphic job openings to LinkedIn: https://www.linkedin.com/posts/brian-anderson-6739ba25_neuromorphic-machinelearning-jobs-activity-7025813029011300352-sClP https://www.linkedin.com/posts/brian-anderson-6739ba25_neuro... My team at Intel Labs is hiring, as are a number of well-funded startups.

zozbot234 4y ago

Analog computing sucks. It's inherently sensitive to noise and random variation in your devices that's basically ubiquitous with modern chip-making processes. Digital electronics actively reject noise at every step in a computation, albeit at the cost of wasting energy in the process. With analog, a more complex computation becomes exponentially harder.

silvestrov 4y ago
Analog computers are like LP records: only good for nostalgia.
- nanna 4y ago
  Depth of sound, long term storage, ease of navigation, there are lots of reasons people prefer vinyl that aren't based in nostalgia.
  - foobarbecue 4y ago
    Depth? You mean like a large range in available amplitudes (loud vs soft)?
    - alar44 4y ago
      Probably, and they're wrong.
  - BitwiseFool 4y ago
    No DRM and the fact that you actually own it is a big plus.
    - tinus_hn 4y ago
      Like a CD?
      - foobarbecue 4y ago

shrubble 4y ago

I think that the author is wrong. Analog computer's weak point is the power supply, such that many companies making them ended up having to manufacture their own to very high standards, such as big capacitors with 0.1% tolerance. Reason is that you are using the analog voltage and thus poor power regulation leads to inaccurate results. With newer analog computer setups more is integrated into the chip itself, making power supply issues much less of a problem.

yummypaint 4y ago
On the other hand, making large passives on silicon is extremely challenging to do accurately, and relatively expensive in terms of area. Most power regulators rely on external caps for this reason.

Aldipower 4y ago

This topic cannot miss a post mentioning Bern Ulmann. Here a video, where's he demonstration the Juksowski profile. https://www.youtube.com/watch?v=nP84Dv01y4A https://www.youtube.com/watch?v=nP84Dv01y4A

gjvc 4y ago

paraffin lamps may be coming back, too

MichaelMoser123 4y ago

isn't a quantum computer a kind of analog computer? Wikipedia says "An analog computer or analogue computer is a type of computer that uses the continuous variation aspect of physical phenomena" https://en.wikipedia.org/wiki/Analog_computer https://en.wikipedia.org/wiki/Analog_computer

red75prime 4y ago
Quantum logic gates perform discrete operations on quantum states of qubits. That is there's a finite number of quantum states that qubits can end up in (in the ideal quantum computer).

ratrocket 4y ago

https://archive.ph/DeK8H https://archive.ph/DeK8H

xor99 4y ago

Analog computers can perform matmul operations without data movement using physical properties such as conductance changes in a very small volume. If noise and random variation can be modelled successfully then in certain cases they are obviously going to be better (e.g. energy use in edge applications). The discussion is not specific enough to applications to be useful. AI data centres are not going to be using this stuff anytime soon for example. On the other hand, you do not want an NVIDIA GPU inserted into your body.

jerf 4y ago

It seems to me that punters overly excited to declare that everything we've ever done up to this point is wrong and there's a new paradigm coming that will obsolete everything, as well as people being critical of the new thing, tend to miss a very important aspect of digital computation that will prevent analog computation from ever simply "taking over": Digital computation is essentially infinite composable. It does not matter how many gates you throw your 1s and 0s through; they will remain ones and zeros. While floating point numerical computations carry challenges, they are at least deterministic challenges. You can build things like an SHA512 hash which can ingest gigabytes upon gigabytes, with the entire computation critically dependent at every step upon all previous computations in the process, a cascading factor of literally billions and billions, and deterministically get the exact same SHA512 hash for the exact same input every time. This property is so reliable that we don't even think about it. Analog computers can not do that. You could never build a hash function like that out of analog parts. You can not take the output of an analog computer and feed it back into the input of another analog computation, and then do it billions upon billions of times, and get a reliable result. Such a device would simply be a machine for generating noise. Analog computing fits into the computing paradigm as another "expansion card". It may take isolated computations and perform them more efficiently. Perhaps even important computations. But they will always be enmeshed in some digital computer paradigm. Breathless reports about how they're "coming back" and coming soon and taking over are just nonsense. (I speak generally, this walled-off article may or may not have made such claims, I dunno.) So many things about how digital computers work that you just take for granted are simply impossible for analog computers, structurally; something as simple as taking a compressed representation of a starting state for your analog computer is something you need a digital computer for, because our best compression algorithms have the same deep data dependencies that I mentioned for the hashing case. Useful, interesting, innovative, gonna make some people some money and create some jobs? Sure. Something we should all go gaga over? No more than a new database coming out. It's going to be a tool, not a paradigm shift.

jefurii 4y ago

Analog computing has been used by musicians for awhile now. Synthesizers are basically analog computers. Bob Moog was an engineer whose genius was figuring out how to connect keyboards to lab equipment and how to hide enough of the guts to make the gear approachable to musicians. West Coast synthesists like Buchla took the opposite approach of appreciating the sound of analog computing for what it is. Synthesizers tried to hide it for awhile behind layers of user interface, but especially with the Eurorack boom of the last decade or so you can really see that synthesizers are simply specialized analog computers. Lots of synth modules openly use the same terminology as analog computing: filters, amplifiers, multipliers, low-pass gate, sample and hold, sequencer, etc. Musicians like Hainbach use actual test equipment in their music. Guitar effect rigs are also basically analog computers. They're just not used for numerical computation. update: The Signal State is a Zach-like game where you solve puzzles by programming analog computers; in was inspired by Eurorack synthesizers.

alar44 4y ago
No, synthesizers and guitar pedals are as much computers as a trumpet. "It translates the lip movement through the horn and amplifies the sound, thusly it's multiplying and therefore a computer!" That is stretching the definition of a computer to absurdity, sorry.

lambdaxymox 4y ago

It's interesting that the black art of analog design never really goes away even from computing. I was reading Geoffrey Hinton's "Mortal Computation" where he briefly speculated towards the end on low power (in watts used, not capability) neural networks being embedded in hardware via something like memristor networks. It makes me imagine neural networks being distributed as a template (i.e. a blob of XML describing the topology and the weights) and then the network weights get tuned a little different to each device or device model to get the best performance per watt out of them. Since every device is physically a little bit different from the next, in precision analog applications with discrete components the components have to be matched. This is often the case in assembling differential pairs in analog audio circuits. Similarly with the control system parameters with hard drives. Since each DC motor is a little bit different from the next, the control loop gets tuned at the factory to each drive. So in this case, the neural network gets matched to the circuit it's embedded in. Mortal computation indeed, each neural network becomes truly unique. I could be full of it, but it's fun to imagine at least.

rasz 4y ago

From Travis Blalock (first real optical mouse) Oral History: "each array element had nearest neighbor connectivity so you would calculate nine correlations, an autocorrelation and eight cross-correlations, with each of your eight nearest neighbors, the diagonals and the perpendicular, and then you could interpolate in correlation space where the best fit was. " "And the reason we did difference squared instead of multiplication is because in the analog domain I could implement a difference-squared circuit with six transistors and so I was like “Okay, six transistors. I can’t do multiplication that cheaply so sold, difference squared, that’s how we’re going to do it.” "little chip running in the 0.8 micron CMOS could do the equivalent operations per second to 1-1/2 giga operations per second and it was doing this for under 200 milliwatts, nothing you could have approached at that time in the digital domain." Avago H2000 chip did all the heavy lifting _in analog domain_. No DSP, it was too expensive for digital domain (cost of first civilian handheld GPS receivers also doing heavy autocorrelation, 1998 Garmin StreetPilot was $400-550 retail).

ghoul2 4y ago

As I understand it, analog computing is entirely impractical simply from a Information Theoretic viewpoint. For a signal to convey 8-bits worth of information, it will need to have 256 distinct levels. It we want the signal to range from, lets say, 0 to 5v (which is already quite high), each level only has about 2mV range. This much can easily come from cross-talk, EMI and power supply noise. So all your logic/calculation will be wrong. Once you start talking about 16 bits, it becomes entirely ridiculous: we now can only have 75uV range for each level. This is getting into RF interference territory - just receiving a phone call close to such an analog signal would disrupt it. The way I understand it, there is simply not enough SNR available in our electronics (on die traces or PCB traces) for analog computing to work. Thats why we restrict the number of level we use in our signal: digital being just two level, but even with higher level-counts, we typically use 4 levels or 8. This is somewhat analog, but not really. I am not an EE, so I am entirely open to being corrected on this.