  Domain-specific neural network accelerators have seen growing interest in
recent years due to their improved energy efficiency and inference performance
compared to CPUs and GPUs. In this paper, we propose a novel cross-layer
optimized neural network accelerator called CrossLight that leverages silicon
photonics. CrossLight includes device-level engineering for resilience to
process variations and thermal crosstalk, circuit-level tuning enhancements for
inference latency reduction, and architecture-level optimization to enable
higher resolution, better energy-efficiency, and improved throughput. On
average, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher
performance-per-watt at 16-bit resolution than state-of-the-art photonic deep
learning accelerators.
