  Training deep learning networks involves continuous weight updates across the
various layers of the deep network while using a backpropagation algorithm
(BP). This results in expensive computation overheads 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 build 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 article, we propose a novel photonics-based
backpropagation accelerator for high performance deep learning training. We
present the design for a convolutional neural network, BPLight-CNN, which
incorporates the silicon photonics-based backpropagation accelerator.
BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN
architecture for end-to-end training and prediction. We evaluate BPLight-CNN
using a photonic CAD framework (IPKISS) on deep learning benchmark models
including LeNet and VGG-Net. The proposed design achieves (i) at least 34x
speedup, 34x improvement in computational efficiency, and 38.5x energy savings,
during training; and (ii) 29x speedup, 31x improvement in computational
efficiency, and 38.7x improvement in energy savings, during inference compared
to the state-of-the-art designs. All these comparisons are done at a 16-bit
resolution; and BPLight-CNN achieves these improvements at a cost of
approximately 6% lower accuracy compared to the state-of-the-art.
