Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908548366073856 |
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| author | Fu, Siqing Wu, Lizhou Li, Tiejun Zhang, Chunyuan Zhang, Jianmin Ma, Sheng |
| author_facet | Fu, Siqing Wu, Lizhou Li, Tiejun Zhang, Chunyuan Zhang, Jianmin Ma, Sheng |
| contents | Biologically-inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of Hopfield network for the function of associative memory. Spin-NeuroMem is equipped with energy-efficient spintronic synapses which utilize magnetic tunnel junctions (MTJs) to store weight matrices of multiple associative memories. The proposed synapse design achieves as low as 17.4% power consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem also encompasses a novel voltage converter with a 53.3% reduction in transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5Mx speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work paves the way for the development of energy-efficient and scalable neuromorphic computing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices Fu, Siqing Wu, Lizhou Li, Tiejun Zhang, Chunyuan Zhang, Jianmin Ma, Sheng Hardware Architecture Emerging Technologies Applied Physics Biologically-inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of Hopfield network for the function of associative memory. Spin-NeuroMem is equipped with energy-efficient spintronic synapses which utilize magnetic tunnel junctions (MTJs) to store weight matrices of multiple associative memories. The proposed synapse design achieves as low as 17.4% power consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem also encompasses a novel voltage converter with a 53.3% reduction in transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5Mx speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work paves the way for the development of energy-efficient and scalable neuromorphic computing systems. |
| title | Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices |
| topic | Hardware Architecture Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2404.02463 |