SqueezeLight: Towards Scalable Optical Neural Networks with Multi-Operand Ring Resonators
Jiaqi Gu1,a, Chenghao Feng1,b, Zheng Zhao2, Zhoufeng Ying3, Mingjie Liu1,c, Ray T. Chen1,d, and David Z. Pan1,e
1ECE Department, The University of Texas at Austin
ajqgu@utexas.edu
bfengchenghao1996@utexas.edu
cjay liu@utexas.edu
dchen@ece.utexas.edu
edpan@ece.utexas.edu
2Synopsys, Inc.
zhengzhao@utexas.edu
3Alpine Optoelectronics, Inc.
zfying@utexas.edu
ABSTRACT
Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption. However, due to high area cost and lack of efficient sparsity exploitation, previous ONN designs fail to provide scalable and efficient neuromorphic computing, which hinders the practical implementation of photonic neural accelerators. In this work, we propose a novel design methodology to enable a more scalable ONN architecture. We propose a nonlinear optical neuron based on multi-operand ring resonators to achieve neuromorphic computing with a compact footprint, low wavelength usage, learnable neuron balancing, and built-in nonlinearity. The structured sparsity is exploited to support more efficient ONN engines via a fine-grained structured pruning technique. A robustness-aware learning method is adopted to guarantee the variation-tolerance of our ONN. Simulation and experimental results show that the proposed ONN achieves one-order-of-magnitude improvement in compactness and efficiency over previous designs with high fidelity and robustness.
