Abstract
Most deep learning accelerators in the literature focus only on improving the design of inference phase. We propose a novel photonics-based backpropagation accelerator for high performance deep learning training. The proposed MEMTONIC architecture is a first-of-its-kind memristor-integrated photonics-based deep learning architecture for end-to-end training and prediction. We evaluate the architecture using a photonic 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.
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