A symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning
CoRR(2024)
摘要
Photonic integrated circuits are emerging as a promising platform for
accelerating matrix multiplications in deep learning, leveraging the inherent
parallel nature of light. Although various schemes have been proposed and
demonstrated to realize such photonic matrix accelerators, the in-situ training
of artificial neural networks using photonic accelerators remains challenging
due to the difficulty of direct on-chip backpropagation on a photonic chip. In
this work, we propose a silicon microring resonator (MRR) optical crossbar
array with a symmetric structure that allows for simple on-chip
backpropagation, potentially enabling the acceleration of both the inference
and training phases of deep learning. We demonstrate a 4 × 4 circuit on
a Si-on-insulator (SOI) platform and use it to perform inference tasks of a
simple neural network for classifying Iris flowers, achieving a classification
accuracy of 93.3
on-chip backpropagation and achieve an accuracy of 91.1
task after training. This work contributes to the realization of compact and
energy-efficient photonic accelerators for deep learning.
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