An Electro-Photonic System for Accelerating Deep Neural Networks

The number of parameters in deep neural networks (DNNs) is scaling at about 5$\times$ the rate of Moore's Law. To sustain the pace of growth of the DNNs, new technologies and computing architectures are needed. Photonic computing systems are promising avenues, since they can perform the dominant general matrix-matrix multiplication (GEMM) operations in DNNs at a higher throughput than their electrical counterpart. However, purely photonic systems face several challenges including a lack of photonic memory, the need for conversion circuits, and the accumulation of noise. In this paper, we propose a hybrid electro-photonic system realizing the best of both worlds to accelerate DNNs. In contrast to prior work in photonic and electronic accelerators, we adopt a system-level perspective. Our electro-photonic system includes an electronic host processor and DRAM, and a custom electro-photonic hardware accelerator called ADEPT. The fused hardware accelerator leverages a photonic computing unit for performing highly-efficient GEMM operations and a digital electronic ASIC for storage and for performing non-GEMM operations. We also identify architectural optimization opportunities for improving the overall ADEPT's efficiency. We evaluate ADEPT using three state-of-the-art neural networks-ResNet-50, BERT-large, and RNN-T-to show its general applicability in accelerating today's DNNs. A head-to-head comparison of ADEPT with systolic array architectures shows that ADEPT can provide, on average, 7.19$\times$ higher inference throughput per watt.