Abstract:<div>Deep Neural Networks (DNNs) are increasingly being used in a variety of applications. However, DNNs have huge computational and memory requirements. One way to reduce these requirements is to sparsify DNNs by using smoothed LASSO (Least Absolute Shrinkage and Selection Operator) functions. In this paper, we show that for the same maximum error with respect to the LASSO function, the sparsity values obtained using various smoothed LASSO functions are similar. We also propose a layer-wise DNN pruning … Show more
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