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Kaist develops next-generation ultra-low power LLM accelerator
- moffkalast 3y ago> The 4.5-mm-square chip, developed using Korean tech giant Samsung Electronics Co.'s 28 nanometer process, has 625 times less power consumption compared with global AI chip giant Nvidia's A-100 GPU, which requires 250 watts of power to process LLMs, the ministry explained. >processes GPT-2 with an ultra-low power consumption of 400 milliwatts and a high speed of 0.4 seconds Not sure what's the point on comparing the two, an A100 will get you a lot more speed than 2.5 tokens/sec. GPT 2 is just a 1.5B param model, a Pi 4 would get you more tokens per second with just CPU inference. Still, I'm sure there's improvements to be made and the direction is fantastic to see, especially after Coral TPUs have proven completely useless for LLM and whisper acceleration. Hopefully it ends up as something vaguely affordable.
- dloss 3y agoWhich of the model requirements of Coral TPUs [1] are the most problematic for LLMs? [1] https://coral.ai/docs/edgetpu/models-intro/#model-requirements https://coral.ai/docs/edgetpu/models-intro/#model-requiremen...
- semisight 3y agoGuessing as to what the GP meant--coral TPUs max out around 8M parameters, IIRC. That's a few orders of magnitude less than the smallest LLM model.
- moffkalast 3y agoThe part where they have like 3 bytes of memory so you switch from extremely high latency of RAM to laughably sluggish latency of USB serial. I think there's also no support below 8 bit quants, which you'd really need.
- dartos 3y ago> New structure mimics the layout of neurons and synapses What does that mean, practically? How can you mimic that layout in silicon?
- p1esk 3y agoThis means they use Spiking Neural Networks. It’s a software algorithm that most likely doesn’t work as well as regular NNs.
- colinator 3y agoWell, our brains are closer to spiking neural networks than 'regular' neural networks. And they work pretty well. For the most part. I feel like SNNs are like Brazil - they are the future, and shall remain so. I think more basic research is needed for them to mature. AFAIK the current SOTA is to train them with 'surrogate gradients', which shoe-horn them into the current NN training paradigm, and that sort of discards some of their worth. Have biologically-inspired learning rules, like STDP, _really_ been exhausted?
- ilaksh 3y agoBut this group claims to have demonstrated a way to use SNNs to run LLMs effectively and with vastly less energy usage.
- p1esk 3y agoIf OpenAI or DeepMind makes such claim I'd pay attention. Otherwise it's always some (usually hw) guys trying to get a grant, or even just publish a paper. p.s. People interested in biologically inspired data processing algorithms should look at Numenta's papers (earlier ones, because recently they switched to regular deep learning), and especially learn their justification for not using spikes.
- esafak 3y agoThey switched? Why? There goes their whole raison d'etre!
- geuis 3y agoWant to reference Groq.com. They are developing their own inference hardware called an LPU https://wow.groq.com/lpu-inference-engine/ https://wow.groq.com/lpu-inference-engine/ They also released their API a week or 2 ago. Its significantly faster than anything from OpenAI right now. Mixtral 8x7b operates at around 500 tokens per second. https://groq.com/ https://groq.com/
- moffkalast 3y agoIt's not so much an accelerator as it is addressing the main inference bottleneck (i.e. memory latency) with sheer brute force by throwing money at the problem. They've made accelerators out of pure L3 cache with a whopping 230 MB per card. They cited something like 500 cards to load one single Mixtral instance, which probably cost over $10M to build. It's a supercomputer essentially.
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- jiggawatts 3y agoOr to put it another way: they’ve made a compute substrate with the correct ratios of processing power to memory capacity. NVIDIA GPUs were optimised for different workloads, such as 3D rendering, that have different optimal ratios. This “supercomputer” isn’t brute force or wasteful because it allows more requests per second. By having each response get processed faster it can pipeline more of them through per unit time and unit silicon area.
- wmf 3y agoThe correct ratio for one workload (production inference).
- cavisne 3y agoA recent presentation on the architecture https://youtu.be/WQDMKTEgQnY?si=W0E9Kq6P280l3Wcl https://youtu.be/WQDMKTEgQnY?si=W0E9Kq6P280l3Wcl IMO we still need an MLPerf submission or similar to really understand if this is more efficient or more efficient only if you also want to minimize latency. Nvidia has pulled enough rabbits out of the hat when it comes to MLPerf I’m still not convinced they can’t work some CUDA magic and undercut them on efficiency.
- zachbee 3y agoNeuromorphic computing is cool, but not new tech. However, using a neuromorphic spiking architecture to run LLMs seems new. Unfortunately, there doesn't seem to be a paper associated with this work, so there's no deeper information on what exactly they're doing.
- imtringued 3y agoI heavily doubt that they are running LLMs on this.
- zachbee 3y agoThe article says they ran GPT-2! Which isn't particularly large, but replicating a large language model with a spiking neural network seems like novel work at least.
- sroussey 3y agoIs their work similar to Rain.ai then?
- bglazer 3y agoThe article says 400 milliwatt power draw. Wolfram Alpha says thats roughly equivalent to cell phone power draw when sleeping.
- pavelstoev 3y agoWe build software acceleration for LLM, effectively running smaller llama2 models at the same performance on several L4's as on 1xA100.
- PeterStuer 3y agoQuick shoutout to https://youtube.com/@TechTechPotato https://youtube.com/@TechTechPotato for those interested in keeping tabs on the AI hardware space. There is much more going on in this area than you would think if you only follow general media.