BPhoton-CNN: An Ultrafast Photonic Backpropagation Accelerator for Deep Learning

Abstract

Training deep learning networks involves continuous weight updates across its many using a backpropagation algorithm (BP). This results in expensive computation and energy overhead during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to develop a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this paper, we introduce a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPhoton-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPhoton-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPhoton-CNN using a commercial CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves at least 35× acceleration in training time, 31× improvement in computational efficiency, and 45× energy savings compared to the state-of-the-art designs, without any loss of accuracy.