Diffusive Memristors with Uniform and Tunable Relaxation Time for Spike Generation in Event-Based Pattern Recognition

被引：79

作者：

Ye, Fan ^{[1
]}

Kiani, Fatemeh ^{[1
]}

Huang, Yi ^{[1
]}

Xia, Qiangfei ^{[1
]}

机构：

[1] Univ Massachusetts, Dept Elect & Comp Engn, Amherst, MA 01003 USA

来源：

ADVANCED MATERIALS | 2023年 / 35卷 / 37期

基金：

美国国家科学基金会;

关键词：

diffusive memristors; neuromorphic computing; relaxation time; spiking neural networks; time surfaces; CROSSBAR ARRAYS; INTEGRATION; HIERARCHY;

D O I：

10.1002/adma.202204778

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

A diffusive memristor is a promising building block for brain-inspired computing hardware. However, the randomness in the device relaxation dynamics limits the wide-range adoption of diffusive memristors in large arrays. In this work, the device stack is engineered to achieve a much-improved uniformity in the relaxation time (standard deviation sigma reduced from approximate to 12 to approximate to 0.32 ms). The memristor is further connected with a resistor or a capacitor and the relaxation time is tuned between 1.13 mu s and 1.25 ms, ranging from three orders of magnitude. The hierarchy of time surfaces (HOTS) algorithm, to utilize the tunable and uniform relaxation behavior for spike generation, is implemented. An accuracy of 77.3% is achieved in recognizing moving objects in the neuromorphic MNIST (N-MNIST) dataset. The work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption.

引用

页数：7