Neuromorphic computing is a promising candidate to accelerate big data processing. Recently, several attempts have been made to design neuromorphic accelerators for popular machine learning algorithms, such as reservoir computing, deep learning, spiking neurons etc. Deep learning accelerator which involves convolutional neural networks (CNNs) have received widespread attention for their accuracy and efficiency. This paper proposes ConvLight, a novel deep learning accelerator based on memristor integrated photonic computing framework. While the use of on-chip photonic circuits for analog computing is well known, no prior work has demonstrated a full-fledged accelerator based on photonic components. In particular, this paper makes the following novel contributions: (i) A multilayer deep learning architecture design is proposed using compute efficient memristors and photonic components for the first time. (ii) A pipelined design for each CNN layer is presented for maximizing throughput and enabling parallelism across the layers. (iii) Simulation of ConvLight architecture with standard photonic tools for demonstrating the execution of DNN and CNN workloads yielding 25X, 60X, and 40X improvements in computational efficiency, throughput, and energy efficiency (respectively) compared to state-of-the-art design.
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