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