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- W2997442807 abstract "The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations. In this position paper, we argue that a simultaneous DNN/implementation co-design methodology, named Neural Architecture and Implementation Search (NAIS), deserves more research attention to boost the development productivity and efficiency of both DNN models and implementation optimization. We propose a stylized design methodology that can drastically cut down the search cost while preserving the quality of the end solution. As an illustration, we discuss this DNN/implementation methodology in the context of both FPGAs and GPUs. We take autonomous driving as a key use case as it is one of the most demanding areas for high quality AI algorithms and accelerators. We discuss how such a co-design methodology can impact the autonomous driving industry significantly. We identify several research opportunities in this exciting domain." @default.
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- W2997442807 date "2019-11-01" @default.
- W2997442807 modified "2023-10-18" @default.
- W2997442807 title "NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving" @default.
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- W2997442807 doi "https://doi.org/10.1109/iccad45719.2019.8942055" @default.
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