Paper: https://arxiv.org/abs/2406.02528 -- always better than a press release. Code: https://github.com/ridgerchu/matmulfreellm --- Like others before them, the authors train LLMs using parameters consisting of ternary digits, or trits, with values in {-1, 0, 1}. What's new is that the authors then build a custom hardware solution on an FPGA and run billion-parameter LLMs consuming only 13W, moving LLM inference clos…
The claim about moving closer to brain-like efficiency conveniently omits how that model compares to modern LLMs. You can put together a toy LLM that is much smaller and more efficient than ChatGPT but isn’t as useful and call it “more efficient”, but that’s not useful in practice.
> Although they reduced the number of operations, the researchers were able to maintain the performance of the neural network by introducing time-based computation in the training of the model. This enables the network to have a “memory” of the important information it processes, enhancing performance. This technique paid off — the researchers compared their model to Meta’s state-of-the-art algorithm called Llama, and were able to achieve the same performance, even at a scale of billions of model parameters.