Monolithic Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment

• URL: http://arxiv.org/abs/2111.06862v1
• Date: Fri, 12 Nov 2021 18:31:51 GMT
• Title: Monolithic Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment
• Authors: Matthew J. Filipovich, Zhimu Guo, Mohammed Al-Qadasi, Bicky A. Marquez, Hugh D. Morison, Volker J. Sorger, Paul R. Prucnal, Sudip Shekhar, and Bhavin J. Shastri
• Abstract summary: We propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation. We experimentally demonstrate training a deep neural network with the MNIST dataset using on-chip MAC operation results.
• Score: 0.6501025489527172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of artificial intelligence (AI) has witnessed tremendous growth in recent years, however some of the most pressing challenges for the continued development of AI systems are the fundamental bandwidth, energy efficiency, and speed limitations faced by electronic computer architectures. There has been growing interest in using photonic processors for performing neural network inference operations, however these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector of each neural network layer in situ, which is the most computationally expensive operation performed during the backward pass. We also experimentally demonstrate training a deep neural network with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.

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