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In 2018 Pete Bannon showed me the first prototype of Tesla's deep learning ASIC (for training). It was sitting out on a desk, attached to a big heat-sink, near
by 37ef_ced3 6y ago
In 2018 Pete Bannon showed me the first prototype of Tesla's deep learning ASIC (for training). It was sitting out on a desk, attached to a big heat-sink, near Elon Musk's empty chair in Tesla's Palo Alto office
They were having trouble writing a compiler for their custom hardware. So I asked, why not just use a GPU, and use Nvidia's libraries and compilers? Bannon became angry and defensive, and insisted that Tesla couldn't become dependent on Nvidia
So that's another reason to build your own hardware
- syntaxing 6y agoDo you think it was the right choice? Like did the custom hardware save money and make them more competitive? They announced that their board can do 144 TFlops but TFlops is such a bad gauge on performance.
- ttul 6y agoThe math for ASICs works out if you’re going to mint hundreds of thousands of chips. Tesla is building 500K cars per year, so they definitely cross the threshold for economic viability. The NRE costs for the best TSMC node is easily >$10M, but that is peanuts for Tesla against 500,000 units. Source: I once spent a year analyzing custom ASICs for Bitcoin mining.
- darksaints 6y agoBut the parent was talking about training. I don't think tesla is training machine learning models in their cars.
- nine_k 6y agoStill it's $20 per car for the cost of training hardware, no matter how many units of this hardware is required, within reason. This is a rather acceptable cost. And they may potentially have an upper hand in development of such ASICs, in-house tools for them, etc. Of course there is a risk that the results won't be impressive. Likely it's a calculated risk.