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Deep learning is highly pervasive in today's data-intensive era. Inparticular, convolutional neural networks (CNNs) are being widely adopted in avariety of fields for superior accuracy. However, computing deep CNNs ontraditional CPUs and GPUs brings several performance and energy pitfalls.Several novel approaches based on ASIC, FPGA, and resistive-memory devices havebeen recently demonstrated with promising results. Most of them target only theinference (testing) phase of deep learning. There have been very limitedattempts to design a full-fledged deep learning accelerator capable of bothtraining and inference. It is due to the highly compute and memory-intensivenature of the training phase. In this paper, we propose LiteCON, a novel analogphotonics CNN accelerator. LiteCON uses silicon microdisk-based convolution,memristor-based memory, and dense-wavelength-division-multiplexing forenergy-efficient and ultrafast deep learning. We evaluate LiteCON using acommercial CAD framework (IPKISS) on deep learning benchmark models includingLeNet and VGG-Net. Compared to the state-of-the-art, LiteCON improves the CNNthroughput, energy efficiency, and computational efficiency by up to 32x, 37x,and 5x respectively with trivial accuracy degradation.

LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)