ROQ: A Noise Aware Quantization Scheme Towards Robust Optical Neural Networks with Low bit Controls
Optical neural networks (ONNs) demonstrate orders of magnitude higher speed in deep learning acceleration than their electronic counterparts. However, limited control precision and device variations induce accuracy degradation in practical ONN implementations. To tackle this issue, we propose a quantization scheme that adapts a full precision ONN to low resolution voltage controls. Moreover, we propose a protective regularization technique that dynamically penalizes quantized weights based on their estimated noise robustness, leading to an improvement in noise robustness. Experimental results show that the proposed scheme effectively adapts ONNs to limited precision controls and device variations. The resultant four layer ONN demonstrates higher inference accuracy with lower variances than baseline methods under various control precisions and device noises.
2020 Design, Automation Test in Europe Conference Exhibition (DATE)