As Hamming suggested in "Art of doing science and engineering", when you want to make something autonomous, you usually have to build a completely different device that solves the same problem, rather than automating the same device.
I wonder. For all the money thrown into self-driving cars research, could we have had an autonomous rail system by now? The technology for mostly-autonomous rail is well understood. Most of the financial cost is in infrastructure to support the system. Seems to me self-driving cars try to short-circuit that infrastructure build-up. They try to "automate the device" rather than "producing an automated system that solves the problem of moving people and goods".
Specifically, I wonder if, for the cost and time spent on CPU-and-engineer-driven research and development of autonomous cars, if we could have had nationwide autonomous rail rolled out by now.
> could we have had an autonomous rail system by now?
We already have autonomous rail systems. Its called positive train control and was fully implemented like a year or two ago (mandated in 2009, but you know how government works, lol) https://en.wikipedia.org/wiki/Positive_train_control
The train conductor has become more-and-more automated to remove the chance of human error. It works with a system of very reliable sensors that indicate where every train engine is on the rails.
Given the huge amount of cargo any particular train has, I don't think there's any intent on cutting the last two humans (the conductor + engineer) out of their job. Their salary costs are miniscule compared to the safety value they deliver, even if the job of driving a train has been almost entirely automated away by now.
We could have possibly automated some existing rail, but I am not altogether certain that doing so would have lead to any significant improvements in cost or efficiency.
Actually laying the infrastructure for mass transit via rail is an entirely different league of cost from what has been dumped into self driving cars.
We have a hard enough time agreeing on how to do light rail transit in places that want it, and then actually getting it done.
Rail is already highly autonomous with external monitoring systems integrated with vehicle control systems, but I'll just go on as if the distinction between what it is now and what are calling "autonomous" is significant.
What problem does autonomous rail solve? The single driver is already a rounding error in total costs. Also, rail is already a controlled environment where collisions are much less likely to happen than road, so the fruit is much higher up the tree on that aspect too.
It seems to me that bringing autonomy to rail would have little on it's bottom line.
> build a completely different device that solves the same problem
I realised this while discussing self-driving cars with my friends.
I used example of Uber Eats. The problem statement is "I don't want to cook" and a reasonably acceptable solution IMO is cloud kitchens + delivery. As opposed to building a cooking robot.
Cloud kitchens could automate 80% of repeatable stuff because it makes sense to solve that problem at scale.
The labor saving advantages of carrying 100 people on the same vehicle are so enormous that there is little motivation if any to quit paying conductors and engineers.
I've always wondered if TCP and networking design could be applied to autonomous traffic... basically think of every car/train as a packet and ensure no collisions...
Which networking protocol best maps to this?
And what if we had smart traffic lights that were aware of every car in an surrounding area of an intersection...
I mean FFS certain tech companies track all vehicles that drive by/near their corporate campuses and report that back to the city...
And that's almost a decade old now...
So apply the same but report the data back to the traffic management system which is also trained on all the traffic patterns for a given intersection to best optimize for their patterns...
Somehow, I'm reminded of the Tsar tank from WW1. The Russians knew that a new weapon of war: an armored car, was necessary to break the stalemate of trench warfare.
This hypothetical armored car needed many features: the most important was that it must be able to move across the muddy no man's land reliably.
Tests have shown that regular sized wheels would get stuck in the mud. A bigger wheel has more surface area and greater contact area. So the Russians built an armored car with the largest wheels possible. Russian tests were outstanding, the Tsar tank rolled over a tree !!!!
The French design was to use caterpillar tracks. We know what works now since we have a century of hindsight.
--------
Spending the most money to make the biggest wheel isn't necessarily the path to victory. I think it's more likely that the tech (aka, caterpillar track equivalent) hasn't been invented yet for robotaxis. Hitting the problem with bigger and more expensive neural network computers doesn't seem to be the right way to solve the problem.
> Of this competition, only Google and Nvidia have supercomputers that stand toe to toe with the Tesla’s
Even assuming that it's true (which I very much doubt - anyone that's willing to spend enough money with Nvidia, can have powerful supercomputer fairly quickly), it's very dishonest statement. It's comparing deployed system with a lab prototype of a single competent of potential supercomputer, that may be fully operational in few years (software is a really, really, really big deal here).
It is really unreasonable to compare Tesla's photoshop mocks with hardware already deployed in the field today. Google already has a TPUv4 cluster that can train ResNet-50 in 13 seconds, which is ridiculous. Until Tesla publishes actual MLPerf benchmarks, you can assume that their ASIC game is at least as far behind Google's as their self-driving game is behind Waymo's: 5 years at a minimum.
It sounds like they're going to have write a ton of custom software in order to use this hardware at scale. And, based on the team being speechless when asked a follow up question, it doesn't sound like they know (yet) how they're going to solve this.
Nvidia gets a lot of credit for their hardware advances, but what really what their chips work so well for deep learning was the huge software stack they created around CUDA.
Underestimating the software investment required has plagued a lot of AI chip startups. It doesn't sound like Tesla is immune to this.
I would argue it’s always useful to see their tech deconstructed and explained. If nothing else, so we get an idea what the reality is to counter possible outlandish claims from overly-enthusiastic followers of the company (and its CEO)
Clearly a a shot across the bow for Cerebras and another excellent target for the GraphBLAS.
Dense numeric processing for image recognition is a key foundation for what Tesla is trying to do, but that tagging of the object is just the beginning of the process, what is the object going to do? What are its trajectories, what is the degree of belief that a unleashed dog vs a stationary baby carriage is going to jump out?
We are just beginning to scratch the surface of counterfactual and other belief propagation models which are hypersparse graph problems at their core. This kind of chip, and what Cerebras are working on, are the future platforms for the possibility of true machine reasoning.
> but the short of it is that their unique system on wafer packaging and chip design choices potentially allow an order magnitude advantage over competing AI hardware in training of massive multi-trillion parameter networks.
I kind of wonder if Tesla is building the Juicero of self-driving. [0]
Beautifully designed. An absolute marvel of engineering. The result of brilliant people with tons of money using every ounce of their knowledge to create something wonderful.
Except... you could just squeeze the bag. You could just use LIDAR. You could just use your hands to squish the fruit and get something just as good. You could just (etc etc).
No doubt future Teslas will be supercomputers on wheels. But what if all those trillions of parameters spent trying to compose 3D worlds out of 2D images is pointless if you can just get a scanner that operates in 3D space to begin with??
The Juicero comparison doesn't hold up. LIDAR is 10x more expensive than RGB, but neither reach lvl5 at the moment.
I'm glad multiple companies try multiple paths, it's the best way to avoid a research dead-end.
Everyone acts like LIDAR is the holy grail but then why isn't there someone destroying Tesla with that tech? Waymo is not much farther along than Tesla, maybe even behind as far as miles driven.
If that was all that was needed then it would be done.
I'm glad people are exploring the design space. To some extent the training techniques and neural net architectures need to be tailored to the hardware. Nvidia isn't on top just because they're good at chip design, but because people have chosen to focus research effort on techniques that work well on Nvidia hardware. New hardware may allow new techniques to shine.
New hardware architectures can't really be used to their full potential without years of research into techniques that are suited for them. The more people who have access to the hardware, the faster we can discover those techniques. If Tesla is serious about their hardware project, they need to offer it to the public as some kind of cloud training system. They don't have enough people internally to develop everything themselves in a short enough time to remain competitive with the rest of the industry.
Trillion parameter networks are mentioned a few times, but Tesla is deploying much smaller networks than that (like tens of millions IMU). Trillion param networks are mostly transformers like GPT-3 (actually 175B) etc... that are particularly heavy vs Conv as they have no weight sharing. Tesla is definitely starting to use transformers though, e.g. for camera fusion and evidenced by their focus on matrix multiply in dojo asic's vs the conv asics they have in the on-vehicle chips.
Yup, there's plenty of ML architectures that try to save on parameters size, achieving better generalization (less overfitting) at the expense of slightly costlier training and inference. The memory constraints on Tesla Dojo might not be a big deal after all.
All I see is [techno terms].. impressive engineering.. lots of problems need to be solved first..2022..on paper toe to toe with Nvidia.. calm the hype.
What a bs fanboy article.. the author is going gaga over something that isnt even out in silicon yet, and has no credible plans of software ecosystem coming on top the hw( if it materializes). Unbelievable hype.
> they have 1.25MB of SRAM and 1TFlop of FP16/CFP8… This is woefully unequipped for the level of performance they want to achieve.
Any idea how OP made that conclusion?
My GeForce 1080Ti has 1.3MB of in-core L1 caches (28 streaming multiprocessors, 48kb L1 each). It also has L2 but not too large, slightly under 3MB for the whole chip.
The GPU delivers about 10 TFlops of FP32 which needs 2x the RAM bandwidth of FP16. I’m generally OK with the level of performance, at least until the GPU shortage is fixed.
> This chip is not Tesla designing something that is better than everyone else all by themselves. We are not at the liberty to reveal the name of their partner(s), but the astute readers will know exactly who we are talking about when we reference the external SerDes and photonics IP.
Any "astute readers" here who know who the partner would be?
My curiosity got piqued at the mention of CFP8 (configurable floating point 8), but googling this didn't yield usable information.
What exactly is CFP8? How many bits does one instance of CFP8 use? What mathematical operations are supported? How does one configure the floating point?
Perhaps CFP8 are parameterized 8-bit posits where the parameter is the value es. The larger es is, the greater the dynamic range is at the expense of precision. Two examples:
posit<8, 0> (es = 0) has as largest positive number 64 and the smallest positive number 1/64.
posit<8, 1> (es = 1) has as largest positive number 4012 and the smallest positive number 1/4012.
The formula for the largest positive number for 8-bit posits is:
2 ^ 2 ^ es ^ 6.
posits don't have NaNs and only one infinity (±∞), so they can use more of the 8 bit values for numbers than floating point numbers.
This is a step in the right direction. I witnessed the semiconductor industry abandoning their own designs in favor of Intel/x86. Better diversity in chip design is always a good thing, even if its in closed ecosystems (Google TPU, Tesla Dojo)
it's interesting because it's clearly exciting & leading edge tech. unlike most Tesla tech which ultimately has consumers using it, where we all get to assess strengths & weaknesses, this tech is going to remain inside the Tesla castle, unviewable, unassessable. we'll probably never know what real strengths or weaknesses it has, never understand all the ways it doesn't well, or as well as competitors. it's going to remain an esoteric dollop of computing.
I confess I have a reflexive skepticism to the idea that Tesla's achievements (and struggles) in car manufacturing would translate to any kind of lead in chip design and manufacturing. How long did it take Apple from planning to rollout for M1? And the Tesla chip seems to be making bigger revolution-sized claims?
Tesla is already shipping their own chips in every car. And that’s a better comparison (it’s an end user thing you can buy) than this data center processor. It’s hard to compare vs, say, Nvidia’s car computers because it’s all locked down. But I believe the energy efficiency is fairly good.
not fully related but i was doing some reading on various "new sustainable ways of transportation" and, since they're building the biggest hyperloop test track near my place, i found this interesting video of some of problems one might get trying to put vacuum in a pipe:
"We believe it makes sense for Tesla to pour as much capital as needed into winning the Robotaxi race and catch up to these two". That has to be a joke right?
At this point, everyone knows the whole thing is a joke. The robotaxi race was supposed to be already finished by 2020 alongside with FSD at Level 5 - today it is still Level 2.
jvanderbot|4 years ago
I wonder. For all the money thrown into self-driving cars research, could we have had an autonomous rail system by now? The technology for mostly-autonomous rail is well understood. Most of the financial cost is in infrastructure to support the system. Seems to me self-driving cars try to short-circuit that infrastructure build-up. They try to "automate the device" rather than "producing an automated system that solves the problem of moving people and goods".
Specifically, I wonder if, for the cost and time spent on CPU-and-engineer-driven research and development of autonomous cars, if we could have had nationwide autonomous rail rolled out by now.
dragontamer|4 years ago
We already have autonomous rail systems. Its called positive train control and was fully implemented like a year or two ago (mandated in 2009, but you know how government works, lol) https://en.wikipedia.org/wiki/Positive_train_control
The train conductor has become more-and-more automated to remove the chance of human error. It works with a system of very reliable sensors that indicate where every train engine is on the rails.
Given the huge amount of cargo any particular train has, I don't think there's any intent on cutting the last two humans (the conductor + engineer) out of their job. Their salary costs are miniscule compared to the safety value they deliver, even if the job of driving a train has been almost entirely automated away by now.
zdragnar|4 years ago
Actually laying the infrastructure for mass transit via rail is an entirely different league of cost from what has been dumped into self driving cars.
We have a hard enough time agreeing on how to do light rail transit in places that want it, and then actually getting it done.
deeviant|4 years ago
What problem does autonomous rail solve? The single driver is already a rounding error in total costs. Also, rail is already a controlled environment where collisions are much less likely to happen than road, so the fruit is much higher up the tree on that aspect too.
It seems to me that bringing autonomy to rail would have little on it's bottom line.
vishnugupta|4 years ago
I realised this while discussing self-driving cars with my friends.
I used example of Uber Eats. The problem statement is "I don't want to cook" and a reasonably acceptable solution IMO is cloud kitchens + delivery. As opposed to building a cooking robot.
Cloud kitchens could automate 80% of repeatable stuff because it makes sense to solve that problem at scale.
whatshisface|4 years ago
samstave|4 years ago
Which networking protocol best maps to this?
And what if we had smart traffic lights that were aware of every car in an surrounding area of an intersection...
I mean FFS certain tech companies track all vehicles that drive by/near their corporate campuses and report that back to the city...
And that's almost a decade old now...
So apply the same but report the data back to the traffic management system which is also trained on all the traffic patterns for a given intersection to best optimize for their patterns...
fanf2|4 years ago
HPsquared|4 years ago
dragontamer|4 years ago
This hypothetical armored car needed many features: the most important was that it must be able to move across the muddy no man's land reliably.
Tests have shown that regular sized wheels would get stuck in the mud. A bigger wheel has more surface area and greater contact area. So the Russians built an armored car with the largest wheels possible. Russian tests were outstanding, the Tsar tank rolled over a tree !!!!
https://en.m.wikipedia.org/wiki/Tsar_Tank
The French design was to use caterpillar tracks. We know what works now since we have a century of hindsight.
--------
Spending the most money to make the biggest wheel isn't necessarily the path to victory. I think it's more likely that the tech (aka, caterpillar track equivalent) hasn't been invented yet for robotaxis. Hitting the problem with bigger and more expensive neural network computers doesn't seem to be the right way to solve the problem.
unknown|4 years ago
[deleted]
zaptrem|4 years ago
justapassenger|4 years ago
Even assuming that it's true (which I very much doubt - anyone that's willing to spend enough money with Nvidia, can have powerful supercomputer fairly quickly), it's very dishonest statement. It's comparing deployed system with a lab prototype of a single competent of potential supercomputer, that may be fully operational in few years (software is a really, really, really big deal here).
solidasparagus|4 years ago
Tesla's compute-to-researcher ratio is definitely rare
jeffbee|4 years ago
https://github.com/mlcommons/training_results_v1.0/tree/mast...
thesausageking|4 years ago
https://www.youtube.com/watch?v=j0z4FweCy4M&t=8047s
It sounds like they're going to have write a ton of custom software in order to use this hardware at scale. And, based on the team being speechless when asked a follow up question, it doesn't sound like they know (yet) how they're going to solve this.
Nvidia gets a lot of credit for their hardware advances, but what really what their chips work so well for deep learning was the huge software stack they created around CUDA.
Underestimating the software investment required has plagued a lot of AI chip startups. It doesn't sound like Tesla is immune to this.
ggoo|4 years ago
snorrah|4 years ago
stcredzero|4 years ago
Can you substantiate this concretely? How about a list, with direct sources? (Not opinion pieces.)
michelpp|4 years ago
Dense numeric processing for image recognition is a key foundation for what Tesla is trying to do, but that tagging of the object is just the beginning of the process, what is the object going to do? What are its trajectories, what is the degree of belief that a unleashed dog vs a stationary baby carriage is going to jump out?
We are just beginning to scratch the surface of counterfactual and other belief propagation models which are hypersparse graph problems at their core. This kind of chip, and what Cerebras are working on, are the future platforms for the possibility of true machine reasoning.
2bitencryption|4 years ago
> but the short of it is that their unique system on wafer packaging and chip design choices potentially allow an order magnitude advantage over competing AI hardware in training of massive multi-trillion parameter networks.
I kind of wonder if Tesla is building the Juicero of self-driving. [0]
Beautifully designed. An absolute marvel of engineering. The result of brilliant people with tons of money using every ounce of their knowledge to create something wonderful.
Except... you could just squeeze the bag. You could just use LIDAR. You could just use your hands to squish the fruit and get something just as good. You could just (etc etc).
No doubt future Teslas will be supercomputers on wheels. But what if all those trillions of parameters spent trying to compose 3D worlds out of 2D images is pointless if you can just get a scanner that operates in 3D space to begin with??
[0] https://www.theguardian.com/technology/2017/sep/01/juicero-s...
arnaudsm|4 years ago
nightski|4 years ago
If that was all that was needed then it would be done.
modeless|4 years ago
New hardware architectures can't really be used to their full potential without years of research into techniques that are suited for them. The more people who have access to the hardware, the faster we can discover those techniques. If Tesla is serious about their hardware project, they need to offer it to the public as some kind of cloud training system. They don't have enough people internally to develop everything themselves in a short enough time to remain competitive with the rest of the industry.
cr4zy|4 years ago
zozbot234|4 years ago
m3kw9|4 years ago
thunkshift1|4 years ago
Const-me|4 years ago
Any idea how OP made that conclusion?
My GeForce 1080Ti has 1.3MB of in-core L1 caches (28 streaming multiprocessors, 48kb L1 each). It also has L2 but not too large, slightly under 3MB for the whole chip.
The GPU delivers about 10 TFlops of FP32 which needs 2x the RAM bandwidth of FP16. I’m generally OK with the level of performance, at least until the GPU shortage is fixed.
neolefty|4 years ago
Any "astute readers" here who know who the partner would be?
Melkman|4 years ago
wumpus|4 years ago
eddanger|4 years ago
_nalply|4 years ago
What exactly is CFP8? How many bits does one instance of CFP8 use? What mathematical operations are supported? How does one configure the floating point?
_nalply|4 years ago
https://www.johndcook.com/blog/2018/04/11/anatomy-of-a-posit...
Perhaps CFP8 are parameterized 8-bit posits where the parameter is the value es. The larger es is, the greater the dynamic range is at the expense of precision. Two examples:
posit<8, 0> (es = 0) has as largest positive number 64 and the smallest positive number 1/64.
posit<8, 1> (es = 1) has as largest positive number 4012 and the smallest positive number 1/4012.
The formula for the largest positive number for 8-bit posits is:
2 ^ 2 ^ es ^ 6.
posits don't have NaNs and only one infinity (±∞), so they can use more of the 8 bit values for numbers than floating point numbers.
I wonder: is CFP8 = posit<8, es>?
scardycat|4 years ago
rektide|4 years ago
it's interesting because it's clearly exciting & leading edge tech. unlike most Tesla tech which ultimately has consumers using it, where we all get to assess strengths & weaknesses, this tech is going to remain inside the Tesla castle, unviewable, unassessable. we'll probably never know what real strengths or weaknesses it has, never understand all the ways it doesn't well, or as well as competitors. it's going to remain an esoteric dollop of computing.
cheinic293|4 years ago
[deleted]
danso|4 years ago
boardwaalk|4 years ago
lpapez|4 years ago
immmmmm|4 years ago
https://youtu.be/Zz95_VvTxZM
gootler|4 years ago
[deleted]
CasillasQT|4 years ago
rvz|4 years ago
So, where are the so-called robotaxis?