1. BitS-Net: Bit-Sparse Deep Neural Network for Energy-Efficient RRAM-Based Compute-In-Memory.
- Author
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Karimzadeh, Foroozan, Yoon, Jong-Hyeok, and Raychowdhury, Arijit
- Subjects
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ENERGY consumption , *ZERO (The number) , *DEEP learning , *COMPUTER architecture , *MOBILE learning - Abstract
The rising popularity of intelligent mobile devices and the computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a novel model compression scheme that allows inference to be carried out using bit-level sparsity, which can be efficiently implemented using in-memory computing macros. In this paper, we introduce a method called BitS-Net to leverage the benefits of bit-sparsity (where the number of zeros are more than number of ones in binary representation of weight/activation values) when applied to compute-in-memory (CIM) with resistive RAM (RRAM) to develop energy efficient DNN accelerators operating in the inference mode. We demonstrate that BitS-Net improves the energy efficiency by up to 5x for ResNet models on the ImageNet dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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