记忆电阻器
横杆开关
油藏计算
计算机科学
可扩展性
多路复用
非常规计算
架空(工程)
计算
乘法(音乐)
并行计算
矩阵乘法
晶体管
计算机体系结构
计算机工程
计算科学
电子工程
分布式计算
电压
人工神经网络
电气工程
算法
人工智能
工程类
物理
循环神经网络
操作系统
量子
数据库
电信
量子力学
声学
作者
Xinxin Wang,Huanglong Li
标识
DOI:10.1088/1361-6528/ad61ee
摘要
Abstract Physical implementations of reservoir computing (RC) based on the emerging memristors have become promising candidates of unconventional computing paradigms. Traditionally, sequential approaches by time-multiplexing volatile memristors have been prevalent because of their low hardware overhead. However, they suffer from the problem of speed degradation and fall short of capturing the spatial relationship between the time-domain inputs. Here, we explore a new avenue for RC using memristor crossbar arrays (MCAs) with device-to-device variations, which serve as physical random weight matrices of the reservoir layers, enabling faster computation thanks to the parallelism of matrix-vector multiplication as an intensive operation in RC. To achieve this new RC architecture, ultralow-current, self-selective memristors are fabricated and integrated without the need of transistors, showing greater potential of high scalability and three-dimensional integrability compared to the previous realizations. The information processing ability of our RC system is demonstrated in tasks of recognizing digit images and waveforms. This work indicates that the “nonidealities” of the emerging memristor devices and circuits are a useful source of inspiration for new computing paradigms.
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