超大规模集成
周转时间
计算机科学
范围(计算机科学)
实施
炸薯条
领域(数学)
计算机体系结构
抽象
算法
人工智能
嵌入式系统
机器学习
计算机工程
制造工程
工程类
软件工程
程序设计语言
电信
哲学
数学
认识论
纯数学
操作系统
作者
Deepthi Amuru,Andleeb Zahra,Harsha V. Vudumula,Pavan K. Cherupally,Sushanth R. Gurram,Amir Ahmad,Zia Abbas
出处
期刊:Integration
[Elsevier BV]
日期:2023-06-16
卷期号:93: 102048-102048
被引量:26
标识
DOI:10.1016/j.vlsi.2023.06.002
摘要
An evident challenge ahead for the integrated circuit (IC) industry is the investigation and development of methods to reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual, time-consuming, and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past toward VLSI design and manufacturing. Moreover, we discuss the future scope of AI/ML applications to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.
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