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arXiv · 2604.09759

Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing

S. Afifi·O. Alo·I. Thakkar·S. Pasricha
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Abstract

Transformers achieve state-of-the-art performance in natural language processing, vision, and scientific computing, but demand high computation and memory. To address these challenges, we present ASTRA, the first silicon-photonic accelerator leveraging stochastic computing for transformers. ASTRA employs novel optical stochastic multipliers and unary/analog homodyne accumulation in a crosstalk-minimal organization to efficiently process dynamic tensor computations. Evaluations show at least 7.6x speedup and 1.3x lower energy overheads compared to state-of-the-art accelerators, highlighting ASTRA's potential for efficient, scalable, and sustainable transformer inference.

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S. Afifi, O. Alo, I. Thakkar, S. Pasricha. 2026-04-10. Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing. https://arxiv.org/abs/2604.09759

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