Energy-efficient XNOR-free In-Memory BNN Accelerator with Input Distribution Regularization
SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks to their compactness in terms of hardware and neural network parameter size. However, previous works had to modify SRAM cells to support XNOR...
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| Published in: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9 |
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Association on Computer Machinery
02.11.2020
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| ISSN: | 1558-2434 |
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| Abstract | SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks to their compactness in terms of hardware and neural network parameter size. However, previous works had to modify SRAM cells to support XNOR operations on memory array resulting in limited area and energy efficiencies. In this work, we present a conversion method which replaces the signed inputs (+1/-1) of BNN with the unsigned inputs (1/0) without computation error, and vice versa. The method enables BNN computing on conventional 6T SRAM arrays and improves area and energy efficiencies. We also demonstrate that further energy saving is possible by skewing the distribution of binary input data based on regularization during network training. Evaluation results show that the proposed techniques improve the inference energy efficiency by up to 9.4x for various benchmarks over previous works. |
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| AbstractList | SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks to their compactness in terms of hardware and neural network parameter size. However, previous works had to modify SRAM cells to support XNOR operations on memory array resulting in limited area and energy efficiencies. In this work, we present a conversion method which replaces the signed inputs (+1/-1) of BNN with the unsigned inputs (1/0) without computation error, and vice versa. The method enables BNN computing on conventional 6T SRAM arrays and improves area and energy efficiencies. We also demonstrate that further energy saving is possible by skewing the distribution of binary input data based on regularization during network training. Evaluation results show that the proposed techniques improve the inference energy efficiency by up to 9.4x for various benchmarks over previous works. |
| Author | Kim, Hyungjun Oh, Hyunmyung Kim, Jae-Joon |
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| Snippet | SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks... |
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| SubjectTerms | binary input data computation error Computational modeling conventional 6T SRAM arrays conversion method energy conservation Energy consumption Energy efficiency energy-efficient edge neural network computing energy-efficient XNOR-free in-memory BNN accelerator Handheld computers inference energy efficiency input distribution regularization memory array network training neural chips neural network parameter size SRAM cells SRAM chips SRAM-based in-memory binary neural network accelerators Training Transistors XNOR operations |
| Title | Energy-efficient XNOR-free In-Memory BNN Accelerator with Input Distribution Regularization |
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