Spin-NeuroMem：一种基于自旋电子器件的低功耗神经形态联想存储器设计
Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices
AI总结：
本文提出基于自旋电子器件的低功耗Hopfield网络联想存储器Spin-NeuroMem，通过MTJ突触和新型电压转换器大幅降低功耗与晶体管用量，并首次实现5M倍加速的联想记忆模拟器。
摘要：
受生物启发的计算模型近年来取得了显著进展，但传统的冯·诺依曼架构对于这些模型所需的大规模矩阵运算和海量并行处理而言效率低下。本文提出了Spin-NeuroMem，一种用于实现联想记忆功能的Hopfield网络低功耗电路设计。Spin-NeuroMem配备了高能效的自旋电子突触，利用磁隧道结（MTJs）存储多个联想记忆的权重矩阵。与最先进的突触设计相比，所提出的突触设计功耗低至17.4%。Spin-NeuroMem还包含一种新型电压转换器，晶体管使用量减少了53.3%，可实现高效的Hopfield网络计算。此外，我们首次提出了一种联想记忆模拟器，在保持可比联想记忆效果的同时实现了5M倍的加速。通过挖掘自旋电子器件的潜力，这项工作为开发高能效且可扩展的神经形态计算系统铺平了道路。
英文摘要：
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.
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