Nothing crazy about it. TPU-like stuff is ~10x the energy efficiency of GPUs and several times the speed. When you're spending megawatt-hours and days to train a single model, it adds up in both real and opportunity costs.
Also, Google TPU TOS prohibits the use of TPUs for stuff that competes with Google (and I'm assuming with other companies under Alphabet umbrella), at Google's sole determination. Not that it would be a good idea to upload Tesla's proprietary data into Google Cloud even if it did not. Cloud, after all, is just somebody else's computer.
> TPU-like stuff is ~10x the energy efficiency of GPUs
10x is probably overstating it when talking about newer GPUs because they have ML hardware in them now. Also, that still doesn't make it a good idea to build your own chips because there will soon be many third party options to choose from. Doing your own chips is a bet that you will out execute dozens of companies ranging from startups to industry giants. Simply taking your pick of the best commercially available options is likely to be a better choice in the near future.
Yep that's the clause. The clause itself is not that problematic for Tesla. What's problematic is that it can be changed over time, and it'd be foolish to single-source something as important as deep learning compute without the option to go elsewhere. Not to mention the rather extravagant Cloud pricing. So Tesla is taking a page out of Steve Jobs' playbook and it will control its own core tech. That's smart, especially considering that they already have bits and pieces of the IP that they'll need.
That clause doesn't apply at all though. It's not even for the same product.
As for single-source, the models are written in PyTorch, not TPU machine code, and they're pretty standard models anyway (e.g. Resnet-50). They can easily transfer to other hardware if necessary. There's not a ton of lock-in there. It doesn't justify the massive costs of ASIC development just to avoid this nonexistent lock-in and imaginary TOS clause.
GPU advantage: more refined ecosystem and you can buy them for $<1000 or get laptops with them built in, and if NVDA has sweat more software engineering blood and tears than GOOG into your model's functions, it will run better on them
TPU advantage: Colab has a free tier that lets you play with them at no charge and if GOOG has sweat more software engineering blood and tears into your model's functions, it will run better on them.
All IMO of course. And deep down it can get more complicated than that, but I salute GOOG for being the first company to ship competitive AI HW, doubly so at scale.
Stuff like their TPU and Waymo's Honeycomb Laserbear (something along those lines... their lidar naming system is pretty long) shows that Google is making good products for a limited reach of people.
TPU? Seems like it has a lot of potential, but not for people directly competing with them.
Waymo's Laserbear lidar? Seems like it has a lot of potential, but not for AV companies directly competing with them.
Google's playing this game pretty fiercely... which given their size is pretty bad/daunting.
>Nothing crazy about it. TPU-like stuff is ~10x the energy efficiency of GPUs and several times the speed. When you're spending megawatt-hours and days to train a single model, it adds up in both real and opportunity costs.
could you share the stats on this? Google told me to use a K-80 for training.
Also, Google TPU TOS prohibits the use of TPUs for stuff that competes with Google (and I'm assuming with other companies under Alphabet umbrella), at Google's sole determination. Not that it would be a good idea to upload Tesla's proprietary data into Google Cloud even if it did not. Cloud, after all, is just somebody else's computer.