2 research outputs found
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very
large-scale integrated (VLSI) is increasing. Although the application of
machine learning (ML) techniques in electronic design automation (EDA) can
trace its history back to the 90s, the recent breakthrough of ML and the
increasing complexity of EDA tasks have aroused more interests in incorporating
ML to solve EDA tasks. In this paper, we present a comprehensive review of
existing ML for EDA studies, organized following the EDA hierarchy.Comment: Accepted by TODAES. The first 10 authors are ordered alphabeticall
A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation
Artificial intelligence (AI) and machine learning (ML) techniques have been
increasingly used in several fields to improve performance and the level of
automation. In recent years, this use has exponentially increased due to the
advancement of high-performance computing and the ever increasing size of data.
One of such fields is that of hardware design; specifically the design of
digital and analog integrated circuits~(ICs), where AI/ ML techniques have been
extensively used to address ever-increasing design complexity, aggressive
time-to-market, and the growing number of ubiquitous interconnected devices
(IoT). However, the security concerns and issues related to IC design have been
highly overlooked. In this paper, we summarize the state-of-the-art in AL/ML
for circuit design/optimization, security and engineering challenges, research
in security-aware CAD/EDA, and future research directions and needs for using
AI/ML for security-aware circuit design