Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices
<|reference_start|>Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices: 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 60% less transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5.05Mx speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work sheds light on the development of energy-efficient and scalable neuromorphic computing systems. The source code will be publicly available after the manuscript is reviewed.<|reference_end|>
@article{fu2024spin-neuromem:,
    title={Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based
  on Spintronic Devices},
    author={Siqing Fu, Tiejun Li, Chunyuan Zhang, Sheng Ma, Jianmin Zhang, Lizhou
  Wu},
    journal={arXiv preprint arXiv:2404.02463},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02463},
    primaryClass={cs.AR cs.ET physics.app-ph}
}