Enhancing DNN Training Efficiency Via Dynamic Asymmetric Architecture
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DOI: 10.1109/lca.2023.3275909
Abstract: Deep neural networks (DNNs) require abundant multiply-and-accumulate (MAC) operations. Thanks to DNNs’ ability to accommodate noise, some of the computational burden is commonly mitigated by quantization–that is, by using lower precision floating-point operations. Layer granularity… read more here.
Keywords: training efficiency; enhancing dnn; dynamic asymmetric; architecture ... See more keywords