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Towards CIM-friendly and Energy-Efficient DNN Accelerator via Bit-level Sparsity

The rising popularity of deep neural network (DNN) algorithms calls for energy-efficient accelerators to enable DNNs run on edge devices. In this paper, we presented BitS-Net, a bit-level sparsity method that quantize the network to desirable numbers with more zeros in their bit representation. We demonstrated that BitS-Net can preserve the accuracy (67.73 %) with accuracy drop ¡1% compared to the original network. Moreover, it achieved up to 5x energy efficiency for ResNet-18 models on the ImageNet dataset compared to the baseline methods.