  The recent breakthroughs and prohibitive complexities of Deep Neural Networks
(DNNs) have excited extensive interest in domain-specific DNN accelerators,
among which optical DNN accelerators are particularly promising thanks to their
unprecedented potential of achieving superior performance-per-watt. However,
the development of optical DNN accelerators is much slower than that of
electrical DNN accelerators. One key challenge is that while many techniques
have been developed to facilitate the development of electrical DNN
accelerators, techniques that support or expedite optical DNN accelerator
design remain much less explored, limiting both the achievable performance and
the innovation development of optical DNN accelerators. To this end, we develop
the first-of-its-kind framework dubbed O-HAS, which for the first time
demonstrates automated Optical Hardware Accelerator Search for boosting both
the acceleration efficiency and development speed of optical DNN accelerators.
Specifically, our O-HAS consists of two integrated enablers: (1) an O-Cost
Predictor, which can accurately yet efficiently predict an optical
accelerator's energy and latency based on the DNN model parameters and the
optical accelerator design; and (2) an O-Search Engine, which can automatically
explore the large design space of optical DNN accelerators and identify the
optimal accelerators (i.e., the micro-architectures and
algorithm-to-accelerator mapping methods) in order to maximize the target
acceleration efficiency. Extensive experiments and ablation studies
consistently validate the effectiveness of both our O-Cost Predictor and
O-Search Engine as well as the excellent efficiency of O-HAS generated optical
accelerators.
