Guest Editorial Dynamical Neuro-AI Learning Systems: Devices, Circuits, Architecture and Algorithms

IF 3.7 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jason K. Eshraghian;Arindam Basu;Corey Lammie;Shih-Chii Liu;Priydarshini Panda
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引用次数: 0

Abstract

This Special Issue of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to demonstrating the latest research progress on dynamical neuro-artificial intelligence (AI) learning systems that bridge the gap between devices, circuits, architectures, and algorithms. The growing demand for AI has spurred the development of systems that: 1) co-localize computation and memory; 2) enhance circuits and devices optimized for operations prevalent in deep learning; and 3) implement lightweight and compressed machine learning models thereby achieving greater accuracy with less resources.
客座编辑 动态神经-人工智能学习系统:设备、电路、架构和算法
本期《IEEE 电路与系统新兴选题期刊》(JETCAS)特刊致力于展示动态神经人工智能(AI)学习系统的最新研究进展,这些系统是设备、电路、架构和算法之间的桥梁。对人工智能日益增长的需求推动了以下系统的发展:1)共同定位计算和内存;2)增强针对深度学习中普遍存在的操作进行优化的电路和设备;3)实施轻量级压缩机器学习模型,从而以更少的资源实现更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.50
自引率
2.20%
发文量
86
期刊介绍: The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.
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