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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00679 |
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| _version_ | 1866916669633331200 |
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| author | Li, Sai Chen, Linliang Zhang, Yihao Zhang, Zhongkui Du, Ao Pan, Biao Wang, Zhaohao Wen, Lianggong Zhao, Weisheng |
| author_facet | Li, Sai Chen, Linliang Zhang, Yihao Zhang, Zhongkui Du, Ao Pan, Biao Wang, Zhaohao Wen, Lianggong Zhao, Weisheng |
| contents | Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices Li, Sai Chen, Linliang Zhang, Yihao Zhang, Zhongkui Du, Ao Pan, Biao Wang, Zhaohao Wen, Lianggong Zhao, Weisheng Applied Physics Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems. |
| title | QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2504.00679 |