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Deep neural networks (DNN) have shown tremendous success in various cognitive tasks, such as image classification, speech recognition, etc. However, their usage on resource-constrained edge devices has been limited due to high computation and large memory requirement. To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compressiondoi:10.1109/icassp.2019.8682791 dblp:conf/icassp/SrivastavaKYBCS19 fatcat:bzs4w7gcgvdzpjyidb54kojkyi