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.

翻译：近年来，受生物学启发的计算模型取得了显著进展，但传统冯·诺依曼架构在处理这些模型所需的大规模矩阵运算和海量并行计算时效率低下。本文提出了Spin-NeuroMem，一种用于联想存储器功能的低功耗霍普菲尔德网络电路设计。Spin-NeuroMem配备了高能效的自旋电子突触，该突触利用磁性隧道结（MTJs）存储多个联想存储器的权重矩阵。与现有最先进的突触设计相比，所提出的突触设计功耗低至其17.4%。Spin-NeuroMem还包含一种新颖的电压转换器，其晶体管使用量减少60%，可有效支持霍普菲尔德网络计算。此外，我们首次提出了一种联想存储器模拟器，在保持可比的联想存储效果的同时，实现了5.05M倍的加速。通过利用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提供了新思路。源代码将在论文审稿后公开。