Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
This academic paper discusses the potential of N:M fine-grained structured sparsity in reducing the computational cost of Deep Neural Networks (DNNs) while maintaining accuracy. It proposes a bidirectional weight pruning…
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