电压控制MTJ-CMOS神经元模拟可调Izhikevich启发动力学

中文摘要 AI 辅助

生物神经元展现出多样的放电动力学，能够实现自适应和刺激依赖的信号传导，然而在硬件中重现这些动力学一直是一个持久的挑战。在本工作中，我们提出了一种受Izhikevich启发的可重构神经元，将电压控制磁隧道结（V-MTJ）动力学与CMOS电路进行协同设计。所提出的架构将V-MTJ的兴奋性动力学（通过可调能量景观实现）与CMOS恢复动力学相结合，以产生五种不同的神经元放电模式，这些模式具有不同的尖峰、爆发和响应特性。我们的结果基于实测的V-MTJ特性和使用商用GlobalFoundries 22纳米FD-SOI CMOS技术的电路仿真，显示每次尖峰的平均能耗为145.44飞焦耳。算法仿真进一步表明，这些放电动力学可将推理尖峰活动减少高达88.6%，同时保持基线分类准确率。这些结果凸显了V-MTJ/CMOS可重构神经元在减少计算活动以及实现紧凑、节能的脑启发计算系统方面的潜力。

英文摘要

Biological neurons exhibit diverse firing dynamics that enable adaptive and stimulus-dependent signalling, yet reproducing these dynamics in hardware has remained an enduring challenge. In this work, we present an Izhikevich- inspired reconfigurable neuron that co-designs voltage-controlled magnetic tunnel junction (V-MTJ) dynamics with CMOS circuitry. The proposed architecture combines V-MTJ excitability dynamics, enabled by a tunable energy landscape, with CMOS recovery dynamics to generate five distinct neuronal firing pat- terns with different spiking, bursting and response characteristics. Our results, based on measured V-MTJ characteristics and circuit simulations using com- mercial GlobalFoundries 22-nm FD-SOI CMOS technology, show an average energy consumption of 145.44 fJ per spike. Algorithmic simulations further show that these firing dynamics reduce inference spike activity by up to 88.6% while maintaining baseline classification accuracy. These results highlight the potential of V-MTJ/CMOS reconfigurable neurons to reduce computational activity and enable compact, energy-efficient brain-inspired computing systems.

发表机构

• University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
• Northwestern University（西北大学）
• George Mason University（乔治梅森大学）

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