The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-memory computing with nanoscale resistive memories may play a crucial role in enabling the execution of AI workloads on these edge devices. In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO$_2$, HfO$_2$-based metal-oxide filamentary synapses, and HfZrO$_4$-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200 neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and ratios between high and low resistance state levels, and proposed mitigation strategies to enhance robustness.

翻译：人工智能在边缘计算设备上的部署面临能耗与功能性的重大挑战。这些设备若采用类脑学习机制，可在低功耗条件下实现实时自适应，从而显著受益。基于纳米级阻变存储器的内存计算可能在支持边缘设备执行人工智能工作负载方面发挥关键作用。本研究提出电压依赖性突触可塑性（VDSP）作为一种基于赫布原理的忆阻突触无监督局部学习高效方法。该方法支持在线学习，无需脉冲时序依赖可塑性（STDP）通常所需的复杂脉冲整形电路。我们展示了VDSP如何适配三种具有不同开关特性的忆阻器件（TiO$_2$基、HfO$_2$基金属氧化物细丝突触以及HfZrO$_4$基铁电隧道结（FTJ））。通过构建包含这些器件的脉冲神经网络进行系统级仿真，在基于MNIST的模式识别任务上验证了无监督学习，实现了最先进的性能。结果表明，使用200个神经元时所有器件均达到超过83%的准确率。此外，我们评估了器件变异性的影响（如开关阈值和高低阻态电平比值），并提出了增强鲁棒性的缓解策略。