Abstract:It is widely known that deep neural networks (DNNs) can perform well in many applications, and can sometimes exceed human ability. However, their cost limits their impact in a variety of realworld applications, such as IoT and mobile computing. Recently, many DNN compression and acceleration methods have been employed to overcome this problem. Most methods succeed in reducing the number of parameters and FLOPs, but only a few can speed up expected inference times because of either the overhead generated from u… Show more
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