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A light-speed large language model accelerator with optical stochastic computing

Abstract

To address the increasingly intensive computational demands of attention-based large language models (LLMs), there is a growing interest in developing energy-efficient and high-speed hardware accelerators. To that end, photonics is being considered as an alternative technology to digital electronics. This work introduces a novel optical hardware accelerator that leverages stochastic computing principles for LLMs. Our proposed accelerator incorporates full-range optical stochastic multipliers and stochastic-analog compute-capable optical-to-electrical transducer units to efficiently handle static and dynamic tensor computations in attention-based models. Our analysis shows that our accelerator exhibits at least 7.6× speedup and 1.3× lower energy compared to state-of-the-art LLMs hardware accelerators.

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transformer neural networks
silicon photonics
inference acceleration
stochastic computing
optical computing

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