神经形态工程学
冯·诺依曼建筑
计算机体系结构
计算机科学
非常规计算
CMOS芯片
缩放比例
晶体管
嵌入式系统
电子工程
人工神经网络
人工智能
分布式计算
电气工程
工程类
几何学
数学
电压
操作系统
作者
Sina Najmaei,Andreu Glasmann,Marshall A. Schroeder,Wendy L. Sarney,Matthew L. Chin,Daniel M. Potrepka
标识
DOI:10.1016/j.mattod.2022.08.017
摘要
The slowing pace of performance improvements in modern processors along with the breakdown of power scaling forecasts an imminent end to the traditional transistor scaling roadmap. Additionally, meeting the aggressive demands of proliferating applications in big-data processing, machine learning, artificial intelligence, and highly distributed edge computing requires radical advancements in materials, devices, and architectures for future processors. Neuromorphic computing has emerged as the most promising successor to conventional complementary metal oxide semiconductor (CMOS) devices and von Neumann architecture. This work reviews the status of neuromorphic research, compares the traditional CMOS approach with neuromorphic devices for implementing biologically inspired circuits, and provides an outlook into integration schemes for future brain-inspired computing hardware.
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