利用ECRAM忆阻器的复杂波动性构建时间曲面
Building time-surfaces by exploiting the complex volatility of an ECRAM memristor
AI总结：
本研究针对基于LiₓWO₃的新型三端ECRAM忆阻器，构建随机模型并探究其STP、双指数衰减等动态特性对HOTS架构精度的影响，发现其可抗噪并提升多时间尺度信息整合精度，方法可推广至其他忆阻器件研究。
摘要：
忆阻器因可作为可编程突触、将计算与存储集成在单个器件中，已成为高效神经形态架构的一项颇具前景的技术。尽管它们最常用于突触权重的静态编码，但近期研究已开始探索利用其动态特性（如短期可塑性，Short Term Plasticity, STP）在基于事件的架构中对事件进行时间积分。然而，我们仍远未完全理解其可能的行为范围，以及这些特性如何在神经形态计算中得到应用。本研究聚焦于一种新开发的基于LiₓWO₃的三端忆阻器，该器件具备可调的STP特性，且其电导响应可通过双指数衰减建模。我们从实验数据推导出该器件的随机模型，并探究了器件随机性、STP以及双指数衰减如何影响时间曲面层次结构（hierarchy of time-surfaces, HOTS）架构的精度。我们发现，器件的随机性不会影响精度；STP能够降低来自事件型传感器的信号中的椒盐噪声影响；而双指数衰减则通过在多个时间尺度上整合时间信息提升了精度。我们的方法可推广用于研究其他忆阻器件，以帮助更好地理解对时间动力学的调控如何让神经形态工程师能够微调器件与架构，以适配其当前面临的问题。
英文摘要：
Memristors have emerged as a promising technology for efficient neuromorphic architectures owing to their ability to act as programmable synapses, combining processing and memory into a single device. Although they are most commonly used for static encoding of synaptic weights, recent work has begun to investigate the use of their dynamical properties, such as Short Term Plasticity (STP), to integrate events over time in event-based architectures. However, we are still far from completely understanding the range of possible behaviors and how they might be exploited in neuromorphic computation. This work focuses on a newly developed Li$_\textbf{x}$WO$_\textbf{3}$-based three-terminal memristor that exhibits tunable STP and a conductance response modeled by a double exponential decay. We derive a stochastic model of the device from experimental data and investigate how device stochasticity, STP, and the double exponential decay affect accuracy in a hierarchy of time-surfaces (HOTS) architecture. We found that the device's stochasticity does not affect accuracy, that STP can reduce the effect of salt and pepper noise in signals from event-based sensors, and that the double exponential decay improves accuracy by integrating temporal information over multiple time scales. Our approach can be generalized to study other memristive devices to build a better understanding of how control over temporal dynamics can enable neuromorphic engineers to fine-tune devices and architectures to fit their problems at hand.
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