Building Time-Surfaces by Exploiting the Complex Volatility of an ECRAM Memristor
Building Time-Surfaces by Exploiting the Complex Volatility of an ECRAM Memristor
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DOI：
10.1109/jetcas.2023.3330832
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发表时间：
2022-01
影响因子：
4.6
通讯作者：
Marco Rasetto;Qingzhou Wan;Himanshu Akolkar;Bertram E. Shi;Feng Xiong;R. Benosman
中科院分区：
文献类型：
--
作者：
Marco Rasetto;Qingzhou Wan;Himanshu Akolkar;Bertram E. Shi;Feng Xiong;R. Benosman
忆阻器作为一种很有前途的高效神经形态架构技术，由于其作为可编程突触的能力，将处理和记忆结合到一个单一的设备中。虽然它们最常用于突触权重的静态编码，但最近的工作已经开始研究它们的动态特性，如短期可塑性（STP），在基于事件的体系结构中随着时间的推移整合事件。然而，我们仍然远远没有完全理解可能的行为范围，以及它们如何在神经形态计算中被利用。这项工作的重点是新开发的基于Li $_{\text {x}}$ WO $_{\text{3}}$的三端忆阻器，它具有可调谐的STP和由双指数衰减建模的电导响应。我们从实验数据中导出了器件的随机模型，并研究了器件随机性、STP和双指数衰减如何影响时间表面层次结构（HOTS）结构的精度。我们发现设备的随机性不影响精度，STP可以减少事件传感器信号中的盐和胡椒噪声的影响，双指数衰减通过在多个时间尺度上整合时间信息来提高精度。我们的方法可以推广到研究其他记忆装置，以更好地理解控制时间动力学如何使神经形态工程师微调装置和架构以适应他们手头的问题。
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 $_{\text {x}}$ WO $_{\text {3}}$ -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.