Building next-generation deep learning hardware using photonic computing

In recent years, the demand for computational power has skyrocketed due to the rapid advancement of artificial intelligence (AI). As we move past Moore’s Law, the limitations of traditional digital computing are pushing the exploration of alternative computing paradigms. Among the emerging technologies, integrated photonics stands out as a highly promising candidate for the next generation of high-performance AI computing as it offers low latency, high bandwidth, and high parallelism. However, there still exist challenges associated with photonic hardware for AI acceleration including the need for slower and less efficient electronic circuits and memory units, lack of efficient nonlinearity in photonics, limited precision, analog noise, and various device non-idealities. In this thesis, we investigate the opportunities and challenges of photonics technology for accelerating state-of-the-art AI workloads from a realistic perspective, evaluate the performance benefits, and propose solutions to address the associated challenges. First, we outline our strategy for designing and evaluating ADEPT, a complete electro-photonic accelerator for deep neural network (DNN) inference. ADEPT leverages a photonic computing unit for general matrix-matrix multiplication (GEMM) operations, a vectorized digital electronic application-specific integrated circuit (ASIC) for non-GEMM operations, and static random-access memory (SRAM) arrays for storing DNN parameters and activations. Unlike previous photonic DNN accelerators, we adopt a system-level perspective to provide a more realistic assessment of the photonics technology and its applicability in accelerating state-of-the-art DNNs. We detail our design steps and introduce optimizations to minimize the overhead of electronic devices. Our evaluation shows that ADEPT achieves, on average, 5.73× higher throughput per watt compared to systolic arrays (SAs), and more than 6.8× and 2.5× better throughput per watt compared to state-of-the-art electronic and photonic accelerators, ...