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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 theirunprecedented potential of achieving superior performance-per-watt. However,the development of optical DNN accelerators is much slower than that ofelectrical DNN accelerators. One key challenge is that while many techniqueshave been developed to facilitate the development of electrical DNNaccelerators, techniques that support or expedite optical DNN acceleratordesign remain much less explored, limiting both the achievable performance andthe innovation development of optical DNN accelerators. To this end, we developthe first-of-its-kind framework dubbed O-HAS, which for the first timedemonstrates automated Optical Hardware Accelerator Search for boosting boththe acceleration efficiency and development speed of optical DNN accelerators.Specifically, our O-HAS consists of two integrated enablers: (1) an O-CostPredictor, which can accurately yet efficiently predict an opticalaccelerator's energy and latency based on the DNN model parameters and theoptical accelerator design; and (2) an O-Search Engine, which can automaticallyexplore the large design space of optical DNN accelerators and identify theoptimal accelerators (i.e., the micro-architectures andalgorithm-to-accelerator mapping methods) in order to maximize the targetacceleration efficiency. Extensive experiments and ablation studiesconsistently validate the effectiveness of both our O-Cost Predictor andO-Search Engine as well as the excellent efficiency of O-HAS generated opticalaccelerators.

O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed