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Tesla is building its own AI chips for self-driving cars

185 points by evo_9 8y ago

gallerdude 8y ago
The benefit they point out is 200fps vs. 2000fps, but is that actually that useful? In the post of OpenAO's robot, they said that improving the response times gave no significant gains. Only thing I can imagine would be allowances for wider and deeper neural nets.
- fhood 8y ago
  More data points can't hurt right? Maybe this is part of their push to move away from third party suppliers?
  - acdha 8y ago
    More data points can hurt: it may just waste time processing data which doesn't improve results but it can actually make things harder if you're just adding more noise which has to be filtered out but there's no corresponding improvement in signal. As a simple example in a different domain, years back I found that the best way to improve Tesseract's OCR accuracy was to ensure that I didn't feed it images at more than 150dpi because it would sometimes misrecognize dust, paper texture, etc. as characters.
    - qaq 8y ago
      My understanding is big problem is stationary objects which mostly get filtered out. With more processing power you might be able to do something about that.
      - acdha 8y ago
        My question is where the break-even point is between adding more resolution / frames-per-second versus performing more expensive analysis. Given that the human visual system is not especially high resolution or speed, I would bet that they're already at the point of diminishing returns on the sensor side and the big wins would come from more expensive processing techniques, which https://news.ycombinator.com/item?id=17671843 https://news.ycombinator.com/item?id=17671843 supports.
- chomp 8y ago
  >2000fps will provide a lot more wiggle room to do higher level computation on each frame. Yeah I think this is it. My best guess, it's 200fps vs 2000fps for the same workload. As Tesla's data model gets larger and its computations get more complex, that 200fps will start to sag. 2000fps will provide a lot more wiggle room to do higher level computation on each frame.
  - bonestamp2 8y ago
    This is exactly right. They didn't mention it in the article, but I was on the call and Elon stated that one of the main benefits was that they could now process all of the camera images at full resolution and full framerate (implying they can't do that with the GPU).
- AbrahamParangi 8y ago
  I bet the performance improvement with higher sampling frequencies is related to the percent of the input signal represented in the sampling (https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampli...)
- reacweb 8y ago
  Maybe that chip was not important in itself but was just a good opportunity to improve in house expertise in chip development. Having expertise gives a lot of power during negociations with providers.
- pasta 8y ago
  Let's say you drive 130km/h (36m/s). 36 / 200 frames = 18cm travel distance between each calculation. So you either can calculate much more each 18cm or calculate shorter distances. I think a shorter distance is not very useful but calculating more might be.
  - auferstehung 8y ago
    *n cameras I think n = 8
  - baybal2 8y ago
    One thing high FPS are good for are optical flow mappers. Think of estimating relative motion of things in video.
- gvb 8y ago
  > Only thing I can imagine would be allowances for wider and deeper neural nets. ...or more cameras, with better frame rates over a lot of cameras. My guess is that the fps rate is over all the cameras, so 10 cameras means going from 20fps per camera to 200fps per camera.
- visarga 8y ago
  I think latency makes a lot of difference when an autonomous system is interacting with the world. Take a look at this video from 2009 where they use a high speed vision system (1000fps!). The things it could do are probably unequaled even today. https://www.youtube.com/watch?v=-KxjVlaLBmk https://www.youtube.com/watch?v=-KxjVlaLBmk The higher the speed of the system, the less it has to predict in the future and better can adapt to unforeseen situations. It's basically using the world as a model, for free.
  - mannykannot 8y ago
    That is impressive, though, in the case of cars, is the minimum prediction not determined by the physics of steering and braking?
coob 8y ago
So much for no new hardware required for L5
- DmenshunlAnlsis 8y ago
  Just wait until they admit that they need LIDAR too... Cha Ching! Various Autopilot accidents strongly imply that whatever the computer “brain” is doing doesn’t matter if it doesn’t have sufficient “eyes” to actually see things like a fire truck, cop car, or bollard.
  - ethbro 8y ago
    And yet squishy meatbags manage to sensor fuse a stereoscopic image (plus nascient sound based imaging) into a reliably rendered picture of the world...
    - DmenshunlAnlsis 8y ago
      Our eyes are incredibly advanced compared to any camera in existence, and hooked up to a real life AGI, and we still fuck up. No comparison. We meatbags are still far more sophisticated and battle tested than anything said meatbags managed to build, which is why so much of our building attempts to replicate our own capabilities.
    - azernik 8y ago
      Computer vision is Not There Yet.
    - TeMPOraL 8y ago
      Yeah, and they do that with dozens of years of accumulated knowledge. Do not have the impression that what you perceive with your eyes is the photons hitting your retina. All that input is heavily fused with decades of experience in dealing with people and the world.
      - azernik 8y ago
        Experience and some of the most sophisticated evolved machinery in the human brain.
titzer 8y ago
This is pretty bad from a hardware/software verification perspective. People who care about correctness can just go home, I guess. It's amazing that we're gonna let these things on the streets. When you weigh almost 2 tons and can go >100mph, it doesn't matter if you have a gun mounted on you or not, you are a weapon.
- ben174 8y ago
  It only has to perform better than a human.
  - deleted 8y ago
    [deleted]
  - titzer 8y ago
    Humans are not remotely programmable.
    - vntok 8y ago
      The advertising industry would beg to differ.
    - IMTDb 8y ago
      Ever heard of the concept of "propaganda" ?
      - mhh__ 8y ago
        There is obviously a difference between hacking a single vehicle and (say) convincing a generation of youth to die for a cause. Also, while I agree that you can "program" a human, you can't program them to jump up and down and touch their toes (so to speak)
  - rohit2412 8y ago
    Better than a human aided by the best driver assistance technology
  - 8y ago
tntn 8y ago
> By having its own silicone Will this mistake never go away?
- Ultimatt 8y ago
  Only when everyone learns a major property of fake breasts isn't that they solve computational problems via the field effect.
  - oliveshell 8y ago
    ...and that semiconductor wafers don’t provide a flexible watertight seal around windows, even if you crush them up and squirt them from a tube.
  - OldSchoolJohnny 8y ago
    How cool if they could though?
  - mrfredward 8y ago
    I'm really hoping that some journalist will do a write up titled "Elon Musk Unveils His New Silicone."
- saalweachter 8y ago
  Maybe that was Tesla's innovation.
melling 8y ago
“Nvidia’s hardware was handling about 200 frames per second, its specialized chip is able to do crunch out 2000 frames per second “with full redundancy and failover”.” What are the real-world benefits going from 200 to 2000 frames a second? 200 seems quite fast.
- Theodores 8y ago
  Nothing seems that fast at 50 m.p.h., however, excuse the mix in units, but each of those 200 frames is the equivalent of 10cm or so along the road. At 2000 frames per second then that is the equivalent of 1cm. I don't make a habit of crashing cars but I did have one glancing head on crash, combined speeds around 125 m.p.h. and it was only centimetres that meant I did not wipe out that family and trash the car I was in. As a consequence I would say that this 10x frame rate really is quite game changing, particularly for on-coming vehicles.
  - deleted 8y ago
    [deleted]
- bonestamp2 8y ago
  I think that comparison is just to illustrate the computational improvements. In other words, the new system is 10x faster. As Elon said on the call, that means they can now process all the cameras at full resolution and full framerate, which can improve accuracy and therefore ability and safety... which sounds like the real world benefits you're looking for.
- jgibson 8y ago
  Multiplexing different camera feeds perhaps? Could could do 200/fps split over 10 different camera feeds.
twtw 8y ago
The most interesting question about this is what their motivation was to justify building an in-house ASIC design team. That isn't a super hard thing to do for a company of Tesla's size, but it is nontrivial and seems like a significant investment. It is probable that they got massive performance improvements by specializing their hardware, but it isn't clear that they couldn't have gotten the same perf improvement by upgrading to the next gen nvidia system (drive Xavier or something like that, the one with Volta + NVDLA).
- new299 8y ago
  Because it's expensive. If you have your own ASIC it's a differentiator that's hard for other players to compete with. They'll take this to potential investors (or stockholders the "market") and say this is a big expensive thing we have that nobody else has. It's expensive and time-consuming for them to replicate and we also own a lot of IP that they would need. You and I might know it's bullshit. That's not true of the investment market in general.
- mrfredward 8y ago
  It's scary when a company with chronic implementation issues decides to take on a hard problem that is way outside its core competency. If they succeed, it means slightly improved efficiency and cutting NVidia's profit margin out of their cost structure. If they fail, it would cost billions, and obscure chip bugs could put lives at risk. That risk/reward doesn't seem worth it unless you really know what you're doing and are confident you will succeed. Of course, it fuels the hype machine in the short term.
  - WhompingWindows 8y ago
    "It's scary when a company with chronic implementation issues decides to take on a hard problem that is way outside its core competency." This seems like the biography of Elon Musk's companies. He gets a crazy hard problem that people say he and his companies can not do, then he tries to implement it, often falling well behind and well short of the goalposts due to lack of foresight and overambitious scheduling. There were naysayers the whole way, including just as loud as there are now if not louder, and yet his companies' achievements and networth continue to climb.
slivym 8y ago
>By having its own silicone, Tesla can build for its own needs at its own pace. If they suddenly recognize something the hardware is lacking, they’re not waiting on someone else to build it. It’s by no means a trivial task — but if they can pull it off without breaking the bank (and Elon says it costs them “the same as the current hardware”), it could end up being a significant strength. I outright snorted at this. Time to market is not why you build a custom chip instead of software. Is this article written by a complete bonehead? Tesla has it's own hardware platform. Anyone with an ounce of sense will know that was always going to happen because of power consumption, cost and performance. The Nvidia Drive platform is designed to be a quick way to get to market with FuSa. The requirements for fully autonomous modes are estimated by some to be over 10x higher than the top CPU/GPU offerings.
- planteen 8y ago
  They have also conflated silicon and silicone, which is humorous.
  - mattigames 8y ago
    Blame the English language for making the name of 2 popular industrial materials with such similar names.
    - overcast 8y ago
      I think I'd blame the English language for the same word havint two different meanings. Not two different words meaning two different things.
      - rjplatte 8y ago
        That's a very Right opinion, even though it may not be right. You do have that right, however. Anyway, I've got to get back to work right away.
    - aphextron 8y ago
      But the materials are not even remotely similar. Silicon is a metallic element. Silicone is a polymer. Anyone who would make that mistake clearly has zero idea what they’re talking about, and it makes me question the entire article.
KaoruAoiShiho 8y ago
Can future gen GPUs bridge the gap? The NVIDIA CEO seemed really confident when asked previously about the challenge that specialized AI chips posed. Is his confidence misplaced/just bluster or what?
- slivym 8y ago
  The hardware platform for self-driving cars isn't just Sensor -> AI Chip -> Steering wheel. There will be custom chips for accelerating some of the hardest parts of the algorithm, but there's no reason not to think there'll be a significant Nvidia chip in there too.
- twtw 8y ago
  Probably current gen GPUs can bridge the gap. The nvidia platform after Drive PX 2 (which is what Tesla ships) uses Volta (with "tensor cores" to speed up fp16) and has a deep learning accelerator (http://nvdla.org http://nvdla.org) for inference (i think both int8 and fp16).
nikofeyn 8y ago
what does he mean with regards to GPUs and CPUs "emulating"? they are computation machines doing computations. an algorithm running on one or the other isn't emulating anything in this context. running on "bare metal" is just an optimization along a certain axis. it isn't any more real of a computation. and talking about this now seems like another desperate attempt at generating hype.
- rhema 8y ago
  I think he is saying that CPUs and GPUs are generalized computational mechanisms. They have increasingly complex overhead that attempt to optimize general problems. By limiting the context of use, specialized hardware can perform better than CPU/GPU. I think his thinking relates strongly to ASIC crypto mining hardware https://en.bitcoin.it/wiki/ASIC https://en.bitcoin.it/wiki/ASIC . "bare metal" is just a shorthand for the level of access to hardware that does not require a kernel/OS.
- Ultimatt 8y ago
  Ultimately its down to dimensionality CPUs (at least originally) were designed for scalar calculation, you loaded something a piece at a time from RAM and did a single scalar calculation before putting it back into RAM, rinse repeat. A GPU works in a more vectorised/matrix level way with essentially a pipeline of linear transforms on a set of data values all at once. The issue with both of these two is they still have a linear memory model, because that works for more serial lower dimension operations, usually three dimensions for a GPU maybe a couple more. Networks or graphs as objects aren't neatly linear in memory or nicely shaped into regular dimensions for doing pipelined vectorised calculations. Though the GPU model is a little closer than the CPU one. Graph algorithms its not random access or serial access either its /structured/ access based on the graphs connections. So ideally you want memory shaped and optimised for this sort of access, as well as computation well linked to utilise this rearchitected RAM. Many companies have been exploring newer architectures for dealing with ANN calculations that inherently benefit from changing the model of memory access and computation. So you can "emulate" as in reformulate the mathematical representation of a graph into a list or a matrix, an adjacency list based algorithm would work well on a CPU and an adjacency matrix based one would work better on a GPU. But neither of those fit perfectly to either model they are just dimensionally the nearest with many tradeoffs you have to make for each.
Fricken 8y ago
Right now, with HW2 Tesla is in kind of an awkward no man's land where their hardware is overkill for basic ADAS, but nowhere near enough for any kind of autonomy that allows the driver to turn their attention away from the driving task. By going to a custom ASIC with inferencing. I have little doubt about Musk's claim that it'll offer a 10x improvement over the current stripped down PX Drive2 variant for deep learning tasks. If I'm to decrypt Musk's branded terminology and take him at his word, then they'll be going from about 8 TOPS to 80 TOPs with that they'll be able to get even deeper into the no man's land that's even more overkill for basic ADAS but still not nearly enough to handle full self driving safely in all conditions, though maybe they'll get L3 highway out of it. Nvidia's upcoming Pegasus board will supposedly do 320 TOPS, and their competitor, Intel/Mobileye's EyeQ5 will likely have comparable specs. These motherboards are designed to replace Robotaxi trunks brimming with ~$150k in liquid cooled compute that draw 3-4000 watts.
- tim333 8y ago
  It's hard to say for sure they are no where near full autonomy in their research although their current vehicles crashing into barriers and fire trucks obviously don't cut it.
  - Fricken 8y ago
    I'm only commenting on what little there is to glean about the compute, but there are so many other red flags about their autonomous development strategy. Crippling per-unit cost constraints, No Lidar, SLAM-lite, ANN heavy, insufficient redundancy and they've got a major data collection problem because they're relying on what they can get over the air. Plus Musk scared away most of their their top engineers.
    - tomp 8y ago
      Serious question- why is LIDAR so important? Obviously it’s possible to drive without it (humans do it), so it’s conceivable that eventually machines will be able to do it as well... So I’m guessing your point is, that won’t happen anytime soon and LIDAR is the fastest way to market; can you confirm/expand/clarify?
squarefoot 8y ago
Curiously there already is (was?) a probably eastern European chip manufacturer named Tesla (search images for "tesla ttl chips") but I wasn't able to find info on the manufacturer and if they're still in business.
- dfox 8y ago
  Czechoslovak Tesla was shared brand of various state-owned electronics companies in the communist era (the companies in fact didn't have common parent company). Several of such companies are still in business. In particular the semiconductor manufacturer Tesla Roznov and later TESLA SEZAM is today a subsidiary of ON Semiconductor (anecdotally they manufacture the power transistors used in Tesla Motors drive units, but I have no way to verify that claim).
- r3bl 8y ago
  You're thinking of this: https://en.wikipedia.org/wiki/Tesla_(Czechoslovak_company) https://en.wikipedia.org/wiki/Tesla_(Czechoslovak_company) It's not a unique name in any shape or form. There's a smartphone "manufacturer" from Serbia that uses Tesla as its brand name: https://tesla.info/en/ https://tesla.info/en/
PoarNeemn 8y ago
Maybe now Teslas will actually brake before crashing into a concrete barrier.
mholt 8y ago
We knew this almost a year ago [1]. Tesla held a panel discussion during NIPS 2017 in Long Beach where Elon Musk, Andrej Karpathy, and Jim Keller talked about AI. (I was at the event.) Jim and Elon talked briefly about how their hardware will overcome the limitations of current GPUs by making the bus massively parallel, along with other interesting tid-bits. Here's the direct link to my notes from the event: https://www.reddit.com/r/teslamotors/comments/7iczp7/elon_musk_confirms_that_tesla_is_building_its_own/dqxylsa/ https://www.reddit.com/r/teslamotors/comments/7iczp7/elon_mu... [1]: https://www.theregister.co.uk/2017/12/08/elon_musk_finally_admits_tesla_is_building_its_own_custom_ai_chips/ https://www.theregister.co.uk/2017/12/08/elon_musk_finally_a...
deleted 8y ago
[deleted]
nickik 8y ago
I would love to know if they are building on RISC-V. That would give them nice integration and no additional cost.
vl 8y ago
As for how they’ll get the chips into existing Teslas, Elon says: “We made it easy to switch out the computer, and that’s all that needs to be done. You take out one computer, and plug in the next. All the connectors are compatible.” I'll believe it when they successfully install it to the previous gen car. More often than not it turns out that some other hardware is incompatible with software improvements or has bugs and full system is not functional anyway without full upgrade.
- LastZactionHero 8y ago
  Really working or not, I'm just happy that an upgrade path crossed their minds. My car is 6 years old, and the console/nav looks so dated I wish they'd just put in dials.
fuddle 8y ago
It's pretty interesting that there a only 2 comments on TechCrunch website, yet nearly 200 comments on HN. :)

185 points by evo_9 8y ago

The benefit they point out is 200fps vs. 2000fps, but is that actually that useful? In the post of OpenAO's robot, they said that improving the response times gave no significant gains. Only thing I can imagine would be allowances for wider and deeper neural nets.

fhood 8y ago
More data points can't hurt right? Maybe this is part of their push to move away from third party suppliers?
- acdha 8y ago
  More data points can hurt: it may just waste time processing data which doesn't improve results but it can actually make things harder if you're just adding more noise which has to be filtered out but there's no corresponding improvement in signal. As a simple example in a different domain, years back I found that the best way to improve Tesseract's OCR accuracy was to ensure that I didn't feed it images at more than 150dpi because it would sometimes misrecognize dust, paper texture, etc. as characters.
  - qaq 8y ago
    My understanding is big problem is stationary objects which mostly get filtered out. With more processing power you might be able to do something about that.
    - acdha 8y ago
      My question is where the break-even point is between adding more resolution / frames-per-second versus performing more expensive analysis. Given that the human visual system is not especially high resolution or speed, I would bet that they're already at the point of diminishing returns on the sensor side and the big wins would come from more expensive processing techniques, which https://news.ycombinator.com/item?id=17671843 https://news.ycombinator.com/item?id=17671843 supports.
chomp 8y ago
>2000fps will provide a lot more wiggle room to do higher level computation on each frame. Yeah I think this is it. My best guess, it's 200fps vs 2000fps for the same workload. As Tesla's data model gets larger and its computations get more complex, that 200fps will start to sag. 2000fps will provide a lot more wiggle room to do higher level computation on each frame.
- bonestamp2 8y ago
  This is exactly right. They didn't mention it in the article, but I was on the call and Elon stated that one of the main benefits was that they could now process all of the camera images at full resolution and full framerate (implying they can't do that with the GPU).
AbrahamParangi 8y ago
I bet the performance improvement with higher sampling frequencies is related to the percent of the input signal represented in the sampling (https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampli...)
reacweb 8y ago
Maybe that chip was not important in itself but was just a good opportunity to improve in house expertise in chip development. Having expertise gives a lot of power during negociations with providers.
pasta 8y ago
Let's say you drive 130km/h (36m/s). 36 / 200 frames = 18cm travel distance between each calculation. So you either can calculate much more each 18cm or calculate shorter distances. I think a shorter distance is not very useful but calculating more might be.
- auferstehung 8y ago
  *n cameras I think n = 8
- baybal2 8y ago
  One thing high FPS are good for are optical flow mappers. Think of estimating relative motion of things in video.
gvb 8y ago
> Only thing I can imagine would be allowances for wider and deeper neural nets. ...or more cameras, with better frame rates over a lot of cameras. My guess is that the fps rate is over all the cameras, so 10 cameras means going from 20fps per camera to 200fps per camera.
visarga 8y ago
I think latency makes a lot of difference when an autonomous system is interacting with the world. Take a look at this video from 2009 where they use a high speed vision system (1000fps!). The things it could do are probably unequaled even today. https://www.youtube.com/watch?v=-KxjVlaLBmk https://www.youtube.com/watch?v=-KxjVlaLBmk The higher the speed of the system, the less it has to predict in the future and better can adapt to unforeseen situations. It's basically using the world as a model, for free.
- mannykannot 8y ago
  That is impressive, though, in the case of cars, is the minimum prediction not determined by the physics of steering and braking?

coob 8y ago

So much for no new hardware required for L5

DmenshunlAnlsis 8y ago
Just wait until they admit that they need LIDAR too... Cha Ching! Various Autopilot accidents strongly imply that whatever the computer “brain” is doing doesn’t matter if it doesn’t have sufficient “eyes” to actually see things like a fire truck, cop car, or bollard.
- ethbro 8y ago
  And yet squishy meatbags manage to sensor fuse a stereoscopic image (plus nascient sound based imaging) into a reliably rendered picture of the world...
  - DmenshunlAnlsis 8y ago
    Our eyes are incredibly advanced compared to any camera in existence, and hooked up to a real life AGI, and we still fuck up. No comparison. We meatbags are still far more sophisticated and battle tested than anything said meatbags managed to build, which is why so much of our building attempts to replicate our own capabilities.
  - azernik 8y ago
    Computer vision is Not There Yet.
  - TeMPOraL 8y ago
    Yeah, and they do that with dozens of years of accumulated knowledge. Do not have the impression that what you perceive with your eyes is the photons hitting your retina. All that input is heavily fused with decades of experience in dealing with people and the world.
    - azernik 8y ago
      Experience and some of the most sophisticated evolved machinery in the human brain.

titzer 8y ago

This is pretty bad from a hardware/software verification perspective. People who care about correctness can just go home, I guess. It's amazing that we're gonna let these things on the streets. When you weigh almost 2 tons and can go >100mph, it doesn't matter if you have a gun mounted on you or not, you are a weapon.

ben174 8y ago
It only has to perform better than a human.
- deleted 8y ago
  [deleted]
- titzer 8y ago
  Humans are not remotely programmable.
  - vntok 8y ago
    The advertising industry would beg to differ.
  - IMTDb 8y ago
    Ever heard of the concept of "propaganda" ?
    - mhh__ 8y ago
      There is obviously a difference between hacking a single vehicle and (say) convincing a generation of youth to die for a cause. Also, while I agree that you can "program" a human, you can't program them to jump up and down and touch their toes (so to speak)
- rohit2412 8y ago
  Better than a human aided by the best driver assistance technology
- 8y ago

tntn 8y ago

> By having its own silicone Will this mistake never go away?

Ultimatt 8y ago
Only when everyone learns a major property of fake breasts isn't that they solve computational problems via the field effect.
- oliveshell 8y ago
  ...and that semiconductor wafers don’t provide a flexible watertight seal around windows, even if you crush them up and squirt them from a tube.
- OldSchoolJohnny 8y ago
  How cool if they could though?
- mrfredward 8y ago
  I'm really hoping that some journalist will do a write up titled "Elon Musk Unveils His New Silicone."
saalweachter 8y ago
Maybe that was Tesla's innovation.

melling 8y ago

“Nvidia’s hardware was handling about 200 frames per second, its specialized chip is able to do crunch out 2000 frames per second “with full redundancy and failover”.” What are the real-world benefits going from 200 to 2000 frames a second? 200 seems quite fast.

Theodores 8y ago
Nothing seems that fast at 50 m.p.h., however, excuse the mix in units, but each of those 200 frames is the equivalent of 10cm or so along the road. At 2000 frames per second then that is the equivalent of 1cm. I don't make a habit of crashing cars but I did have one glancing head on crash, combined speeds around 125 m.p.h. and it was only centimetres that meant I did not wipe out that family and trash the car I was in. As a consequence I would say that this 10x frame rate really is quite game changing, particularly for on-coming vehicles.
- deleted 8y ago
  [deleted]
bonestamp2 8y ago
I think that comparison is just to illustrate the computational improvements. In other words, the new system is 10x faster. As Elon said on the call, that means they can now process all the cameras at full resolution and full framerate, which can improve accuracy and therefore ability and safety... which sounds like the real world benefits you're looking for.
jgibson 8y ago
Multiplexing different camera feeds perhaps? Could could do 200/fps split over 10 different camera feeds.

twtw 8y ago

The most interesting question about this is what their motivation was to justify building an in-house ASIC design team. That isn't a super hard thing to do for a company of Tesla's size, but it is nontrivial and seems like a significant investment. It is probable that they got massive performance improvements by specializing their hardware, but it isn't clear that they couldn't have gotten the same perf improvement by upgrading to the next gen nvidia system (drive Xavier or something like that, the one with Volta + NVDLA).

new299 8y ago
Because it's expensive. If you have your own ASIC it's a differentiator that's hard for other players to compete with. They'll take this to potential investors (or stockholders the "market") and say this is a big expensive thing we have that nobody else has. It's expensive and time-consuming for them to replicate and we also own a lot of IP that they would need. You and I might know it's bullshit. That's not true of the investment market in general.
mrfredward 8y ago
It's scary when a company with chronic implementation issues decides to take on a hard problem that is way outside its core competency. If they succeed, it means slightly improved efficiency and cutting NVidia's profit margin out of their cost structure. If they fail, it would cost billions, and obscure chip bugs could put lives at risk. That risk/reward doesn't seem worth it unless you really know what you're doing and are confident you will succeed. Of course, it fuels the hype machine in the short term.
- WhompingWindows 8y ago
  "It's scary when a company with chronic implementation issues decides to take on a hard problem that is way outside its core competency." This seems like the biography of Elon Musk's companies. He gets a crazy hard problem that people say he and his companies can not do, then he tries to implement it, often falling well behind and well short of the goalposts due to lack of foresight and overambitious scheduling. There were naysayers the whole way, including just as loud as there are now if not louder, and yet his companies' achievements and networth continue to climb.

slivym 8y ago

>By having its own silicone, Tesla can build for its own needs at its own pace. If they suddenly recognize something the hardware is lacking, they’re not waiting on someone else to build it. It’s by no means a trivial task — but if they can pull it off without breaking the bank (and Elon says it costs them “the same as the current hardware”), it could end up being a significant strength. I outright snorted at this. Time to market is not why you build a custom chip instead of software. Is this article written by a complete bonehead? Tesla has it's own hardware platform. Anyone with an ounce of sense will know that was always going to happen because of power consumption, cost and performance. The Nvidia Drive platform is designed to be a quick way to get to market with FuSa. The requirements for fully autonomous modes are estimated by some to be over 10x higher than the top CPU/GPU offerings.

planteen 8y ago
They have also conflated silicon and silicone, which is humorous.
- mattigames 8y ago
  Blame the English language for making the name of 2 popular industrial materials with such similar names.
  - overcast 8y ago
    I think I'd blame the English language for the same word havint two different meanings. Not two different words meaning two different things.
    - rjplatte 8y ago
      That's a very Right opinion, even though it may not be right. You do have that right, however. Anyway, I've got to get back to work right away.
  - aphextron 8y ago
    But the materials are not even remotely similar. Silicon is a metallic element. Silicone is a polymer. Anyone who would make that mistake clearly has zero idea what they’re talking about, and it makes me question the entire article.

KaoruAoiShiho 8y ago

Can future gen GPUs bridge the gap? The NVIDIA CEO seemed really confident when asked previously about the challenge that specialized AI chips posed. Is his confidence misplaced/just bluster or what?

slivym 8y ago
The hardware platform for self-driving cars isn't just Sensor -> AI Chip -> Steering wheel. There will be custom chips for accelerating some of the hardest parts of the algorithm, but there's no reason not to think there'll be a significant Nvidia chip in there too.
twtw 8y ago
Probably current gen GPUs can bridge the gap. The nvidia platform after Drive PX 2 (which is what Tesla ships) uses Volta (with "tensor cores" to speed up fp16) and has a deep learning accelerator (http://nvdla.org http://nvdla.org) for inference (i think both int8 and fp16).

nikofeyn 8y ago

what does he mean with regards to GPUs and CPUs "emulating"? they are computation machines doing computations. an algorithm running on one or the other isn't emulating anything in this context. running on "bare metal" is just an optimization along a certain axis. it isn't any more real of a computation. and talking about this now seems like another desperate attempt at generating hype.

rhema 8y ago
I think he is saying that CPUs and GPUs are generalized computational mechanisms. They have increasingly complex overhead that attempt to optimize general problems. By limiting the context of use, specialized hardware can perform better than CPU/GPU. I think his thinking relates strongly to ASIC crypto mining hardware https://en.bitcoin.it/wiki/ASIC https://en.bitcoin.it/wiki/ASIC . "bare metal" is just a shorthand for the level of access to hardware that does not require a kernel/OS.
Ultimatt 8y ago
Ultimately its down to dimensionality CPUs (at least originally) were designed for scalar calculation, you loaded something a piece at a time from RAM and did a single scalar calculation before putting it back into RAM, rinse repeat. A GPU works in a more vectorised/matrix level way with essentially a pipeline of linear transforms on a set of data values all at once. The issue with both of these two is they still have a linear memory model, because that works for more serial lower dimension operations, usually three dimensions for a GPU maybe a couple more. Networks or graphs as objects aren't neatly linear in memory or nicely shaped into regular dimensions for doing pipelined vectorised calculations. Though the GPU model is a little closer than the CPU one. Graph algorithms its not random access or serial access either its /structured/ access based on the graphs connections. So ideally you want memory shaped and optimised for this sort of access, as well as computation well linked to utilise this rearchitected RAM. Many companies have been exploring newer architectures for dealing with ANN calculations that inherently benefit from changing the model of memory access and computation. So you can "emulate" as in reformulate the mathematical representation of a graph into a list or a matrix, an adjacency list based algorithm would work well on a CPU and an adjacency matrix based one would work better on a GPU. But neither of those fit perfectly to either model they are just dimensionally the nearest with many tradeoffs you have to make for each.

Fricken 8y ago

Right now, with HW2 Tesla is in kind of an awkward no man's land where their hardware is overkill for basic ADAS, but nowhere near enough for any kind of autonomy that allows the driver to turn their attention away from the driving task. By going to a custom ASIC with inferencing. I have little doubt about Musk's claim that it'll offer a 10x improvement over the current stripped down PX Drive2 variant for deep learning tasks. If I'm to decrypt Musk's branded terminology and take him at his word, then they'll be going from about 8 TOPS to 80 TOPs with that they'll be able to get even deeper into the no man's land that's even more overkill for basic ADAS but still not nearly enough to handle full self driving safely in all conditions, though maybe they'll get L3 highway out of it. Nvidia's upcoming Pegasus board will supposedly do 320 TOPS, and their competitor, Intel/Mobileye's EyeQ5 will likely have comparable specs. These motherboards are designed to replace Robotaxi trunks brimming with ~$150k in liquid cooled compute that draw 3-4000 watts.

tim333 8y ago
It's hard to say for sure they are no where near full autonomy in their research although their current vehicles crashing into barriers and fire trucks obviously don't cut it.
- Fricken 8y ago
  I'm only commenting on what little there is to glean about the compute, but there are so many other red flags about their autonomous development strategy. Crippling per-unit cost constraints, No Lidar, SLAM-lite, ANN heavy, insufficient redundancy and they've got a major data collection problem because they're relying on what they can get over the air. Plus Musk scared away most of their their top engineers.
  - tomp 8y ago
    Serious question- why is LIDAR so important? Obviously it’s possible to drive without it (humans do it), so it’s conceivable that eventually machines will be able to do it as well... So I’m guessing your point is, that won’t happen anytime soon and LIDAR is the fastest way to market; can you confirm/expand/clarify?

squarefoot 8y ago

Curiously there already is (was?) a probably eastern European chip manufacturer named Tesla (search images for "tesla ttl chips") but I wasn't able to find info on the manufacturer and if they're still in business.

dfox 8y ago
Czechoslovak Tesla was shared brand of various state-owned electronics companies in the communist era (the companies in fact didn't have common parent company). Several of such companies are still in business. In particular the semiconductor manufacturer Tesla Roznov and later TESLA SEZAM is today a subsidiary of ON Semiconductor (anecdotally they manufacture the power transistors used in Tesla Motors drive units, but I have no way to verify that claim).
r3bl 8y ago
You're thinking of this: https://en.wikipedia.org/wiki/Tesla_(Czechoslovak_company) https://en.wikipedia.org/wiki/Tesla_(Czechoslovak_company) It's not a unique name in any shape or form. There's a smartphone "manufacturer" from Serbia that uses Tesla as its brand name: https://tesla.info/en/ https://tesla.info/en/

PoarNeemn 8y ago

Maybe now Teslas will actually brake before crashing into a concrete barrier.

mholt 8y ago

We knew this almost a year ago [1]. Tesla held a panel discussion during NIPS 2017 in Long Beach where Elon Musk, Andrej Karpathy, and Jim Keller talked about AI. (I was at the event.) Jim and Elon talked briefly about how their hardware will overcome the limitations of current GPUs by making the bus massively parallel, along with other interesting tid-bits. Here's the direct link to my notes from the event: https://www.reddit.com/r/teslamotors/comments/7iczp7/elon_musk_confirms_that_tesla_is_building_its_own/dqxylsa/ https://www.reddit.com/r/teslamotors/comments/7iczp7/elon_mu... [1]: https://www.theregister.co.uk/2017/12/08/elon_musk_finally_admits_tesla_is_building_its_own_custom_ai_chips/ https://www.theregister.co.uk/2017/12/08/elon_musk_finally_a...

deleted 8y ago

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nickik 8y ago

I would love to know if they are building on RISC-V. That would give them nice integration and no additional cost.

vl 8y ago

As for how they’ll get the chips into existing Teslas, Elon says: “We made it easy to switch out the computer, and that’s all that needs to be done. You take out one computer, and plug in the next. All the connectors are compatible.” I'll believe it when they successfully install it to the previous gen car. More often than not it turns out that some other hardware is incompatible with software improvements or has bugs and full system is not functional anyway without full upgrade.

LastZactionHero 8y ago
Really working or not, I'm just happy that an upgrade path crossed their minds. My car is 6 years old, and the console/nav looks so dated I wish they'd just put in dials.

fuddle 8y ago

It's pretty interesting that there a only 2 comments on TechCrunch website, yet nearly 200 comments on HN. :)