Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking
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| Format: | Preprint |
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2024
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| _version_ | 1866909680292331520 |
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| author | Farias, Matheus Kung, H. T. |
| author_facet | Farias, Matheus Kung, H. T. |
| contents | We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict the number of times they can be reprogrammed.
To reduce reprogramming demands, we employ two techniques: (1) we organize weights into sorted sections to schedule reprogramming of similar crossbars, maximizing memristor state reuse, and (2) we reprogram only a fraction of randomly selected memristors in low-order columns, leveraging their bit-level distribution and recognizing their relatively small impact on model accuracy.
We evaluate our approach for state-of-the-art models on the ImageNet-1K dataset. We demonstrate a substantial reduction in crossbar reprogramming by 3.7x for ResNet-50 and 21x for ViT-Base, while maintaining model accuracy within a 1% margin. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_21730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking Farias, Matheus Kung, H. T. Hardware Architecture Artificial Intelligence Emerging Technologies Machine Learning We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict the number of times they can be reprogrammed. To reduce reprogramming demands, we employ two techniques: (1) we organize weights into sorted sections to schedule reprogramming of similar crossbars, maximizing memristor state reuse, and (2) we reprogram only a fraction of randomly selected memristors in low-order columns, leveraging their bit-level distribution and recognizing their relatively small impact on model accuracy. We evaluate our approach for state-of-the-art models on the ImageNet-1K dataset. We demonstrate a substantial reduction in crossbar reprogramming by 3.7x for ResNet-50 and 21x for ViT-Base, while maintaining model accuracy within a 1% margin. |
| title | Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking |
| topic | Hardware Architecture Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2410.21730 |