Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing

• URL: http://arxiv.org/abs/2604.09759v1
• Date: Fri, 10 Apr 2026 17:18:26 GMT
• Title: Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing
• Abstract summary: ASTRA is the first silicon-photonic accelerator for transformers.<n>Transformers achieve state-of-the-art performance in natural language processing, vision, and scientific computing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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