10 ms·
TinyTinyTPU: 2×2 systolic-array TPU-style matrix-multiply unit deployed on FPGA
- hinkley 9mo agoI think I could trust AI more if we used it to do heuristics for expensive deterministic processes. Sort of a cross between Bloom Filters and speculative execution. Determine the odds the expensive operation 1 will indicate that expensive operation 2 needs to happen, and then start expensive operation 2 while we determine if it’s actually needed. If its right 95% of the time, which is the sort of ranges AI can aspire to, that’s skipping the high latency task chaining 19 times out of 20, which would be pretty good.
- rjsw 9mo agoThere have been comments that some leading AI researchers were switching away from working on language models to do stuff with "real world data".
- hnuser123456 9mo agoThere are Bayesian neural networks that could apparently track probability rather than just e.g. randomly selecting one output from the top-k based on probability, but I'm still learning up on them myself. Sounds like they're not normally combined with language models.
- inkysigma 9mo agoIirc, the problem with Bayesian neural networks is that they're significantly more difficult to train. Using stuff like SVI reduces a lot of the representational ability of the distribution over weights. It's also questionable how useful the uncertainty over weights is. I suppose in the tradition of Bayesian influence, VAEs and the like are still common though.
- 0-_-0 9mo agoCPU branch predictors use neural networks
- hinkley 9mo agoCPU branch predictors aren’t going to run long expensive operations in the background. This is like saying bloom filters are speculative memory fetches. That’s not completely untrue but it misses the point.
- aunty_helen 9mo agoI think it’s only a matter of time before we see asic vendors making TPU devices. Same thing happened with BTC. There was enough money there to spawn an industry. Nvidias 70% margins are too hard to ignore. And if playing on the open market seems too rough, there’s always acquisition potential like what happened to groq.
- NitpickLawyer 9mo agoAren't high end accelerators already closer to ASICs than to og GPUs, tho?
- tonetegeatinst 9mo agoYes, but not as much as you think. A lot of silicon on a GPU is dedicated to upscaling and matrix multiply. Ultimately GPU's main use is multimedia and graphics focused. See all the miners that used to do GPU based mining...or the other niche markets where eventually the cost of custom asic becomes to attractive to ignore even if you as a consume have to handle a few years of growing pains.
- ssivark 9mo ago> Ultimately GPU's main use is multimedia and graphics focused This has long ceased to be true, especially for data center focused gpus from the last few years; the "gpu" moniker is really a misnomer / historical artifact.
- alanma 9mo agohard to argue today's GPUs are really graphics focused anymore in the training / inference race :O really excited about Rubin CPX / Feynman generations, let's see what the LPU does to the inference stack
- deleted 9mo ago[deleted]
- fooblaster 9mo agoGreat! How do you program it?
- alanma 9mo agoA couple core commands in our ISA detailed on our GitHub, map your problem to matrix ops, here's a brief excerpt, but our tpu_compiler and tpu_driver are the core to programming your own: from tpu_compiler import TPUCompiler, TPURuntime class Custom(nn.Module): def __init__(self): super().__init__() self.layer1 = nn.Linear(2, 2, bias=False) self.layer2 = nn.Linear(2, 2, bias=False) def forward(self, x): x = self.layer1(x) x = torch.relu(x) x = self.layer2(x) return x model = train_model(your_data) # compile to the tiny tiny TPU format compiler = TPUCompiler() compiled = compiler.compile(model) # run and enjoy :) runtime = TPURuntime(tpu) result = runtime.inference(compiled, input_data) Will update soon with some better documentation, but hopefully this will get you started! - Alan and Abiral
- ph4evers 9mo agoSuch a cool project! Next one is to run jaxprs via the driver?
- alanma 9mo agoDefinitely thinking about that! Would be very cool to run the JAX / Pallas stack, noted on our end :) - Alan and Abiral
- mrinterweb 9mo agoI've been wondering when we will see general purpose consumer FPGAs, and eventually ASICs, for inference. This reminds me of bitcoin mining. Bitcoin mining started with GPUs. I think I remember a brief FPGA period that transitioned to ASIC. My limited understanding of Google's tensor processing unit chips are that they are effectively a transformer ASIC. That's likely a wild over-simplification of Google's TPU, but Gemini is proof that GPUs are not needed for inference. I suspect GPU inference will come to an end soon, as it will likely be wildly inefficient by comparison to purpose built transformer chips. All those Nvidia GPU-based servers may become obsolete should transformer ASICs become mainstream. GPU bitcoin mining is just an absolute waste of money (cost of electricity) now. I believe the same will be true for GPU-based inference soon. The hundreds of billions of dollars being invested on GPU-based inference seems like an extremely risky bet that ASIC transformers won't happen, although Google has already widely deployed their own TPUs.
- tucnak 9mo agoIt all comes down to memory and fabric bandwidth. For example, the state of the art developer -friendly (PCIe 5.0) FPGA platform is Alveo V80 which rocks four 200G NIC's. Basically, Alveo currently occupies this niche where it's the only platform on the market to allow programmable in-network compute. However, what's available in terms of bandwidth—lags behind even pathetic platforms like Bluefield. Those in the know are aware of what challenges are there to actually saturate it for inference in practical designs. I think, Xilinx is super well-positioned here, but without some solid hard IP it's still a far cry from purpose silicon.
- mrinterweb 9mo agoAs far as I understand all the inference purpose-build silicon out there is not being sold to competitors and kept in-house. Google's TPU, Amazon's Inferentia (horrible name), Microsoft's Maia, Meta's MTIA. It seems that custom inference silicon is a huge part of the AI game. I doubt GPU-based inference will be relevant/competitive soon.
- almostgotcaught 9mo ago
- babl-yc 9mo agoThis is cool. I'm observing a trend of "build a tiny version from the ground-up to understand it" a la Karpathy's micrograd/minGPT. Seems like one of the best ways to learn.
- alanma 9mo agothanks for the kind words of support! definitely taught us a thing or two, hope you enjoyed the ride along - Alan and Abiral
- alanma 9mo agoThanks again for the repost and all the support!! Been a blast and super cool to see the interest, if you want to follow along for more of our writeups, our blog can be found here: https://chewingonchips.substack.com/ https://chewingonchips.substack.com/