| Title: |
Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices |
| Authors: |
Fu, Siqing, Wu, Lizhou, Li, Tiejun, Zhang, Chunyuan, Zhang, Jianmin, Ma, Sheng |
| Publication Year: |
2024 |
| Collection: |
Computer Science
Physics (Other) |
| Subject Terms: |
Computer Science - Hardware Architecture, Computer Science - Emerging Technologies, Physics - Applied Physics |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2404.02463 |
| Accession Number: |
edsarx.2404.02463 |
| Database: |
arXiv |
