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SNrram: an efficient sparse neural network computation architecture based on resistive random-access memory

Wang, Peiqi ; Ji, Yu ; Hong, Chi ; Lyu, Yongqiang ; Wang, Dongsheng ; Xie, Yuan

Proceedings of the 55th Annual Design Automation Conference, 2018, p.1-6

New York, NY, USA: ACM

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Título:
SNrram: an efficient sparse neural network computation architecture based on resistive random-access memory
Autor: Wang, Peiqi ; Ji, Yu ; Hong, Chi ; Lyu, Yongqiang ; Wang, Dongsheng ; Xie, Yuan
Assuntos: Computer systems organization ; Computer systems organization -- Architectures ; Computer systems organization -- Architectures -- Other architectures ; Computer systems organization -- Architectures -- Other architectures -- Neural networks ; Computing methodologies ; Computing methodologies -- Machine learning ; Computing methodologies -- Machine learning -- Machine learning approaches ; Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks
É parte de: Proceedings of the 55th Annual Design Automation Conference, 2018, p.1-6
Descrição: The sparsity in the deep neural networks can be leveraged by methods such as pruning and compression to help the efficient deployment of large-scale deep neural networks onto hardware platforms, such as GPU or FPGA, for better performance and power efficiency. However, for RRAM crossbar-based architectures, the study of efficient methods to consider the network sparsity is still in the early stage. In this study, we propose SNrram, an efficient sparse neural network computation architecture using RRAM, by exploiting the sparsity in both weights and activation. SNrram stores nontrivial weights and organizes them to eliminate zero-value multiplications for better resource utilization. Experimental results show that SNrram can save RRAM resources by 69.8%, reduce the power consumption by 35.9%, and speed up by 2.49× on popular deep learning benchmarks, compared to a state-of-the-art RRAM-based neural network accelerator.
Editor: New York, NY, USA: ACM
Idioma: Inglês

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SNrram: an efficient sparse neural network computation architecture based on resistive random-access memory

Wang, Peiqi ; Ji, Yu ; Hong, Chi ; Lyu, Yongqiang ; Wang, Dongsheng ; Xie, Yuan

New York, NY, USA: ACM

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