Arash Ardakani

Area of Study / Keywords

Computer Engineering Integrated Circuits and Systems

About

Dr. Arash Ardakani is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alberta. His work lies at the intersection of computer hardware, electronic design automation (EDA), and artificial intelligence, with a focus on developing intelligent and efficient methods for designing and verifying digital systems.

He received his PhD in Electrical and Computer Engineering from McGill University and subsequently conducted postdoctoral research in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He also has industry experience in electronic design automation and hardware verification, including working with the Design Verification team at Cadence Design Systems, bridging academic research and practical challenges in modern chip design.

Dr. Ardakani has authored or co-authored more than 30 peer-reviewed publications in leading conferences and journals. His work combines machine learning, hardware design, and optimization to advance the development of next-generation intelligent computing systems and design automation technologies.

Research

Dr. Ardakani’s research focuses on AI-driven electronic design automation, with an emphasis on the verification, synthesis, and optimization of digital systems. His research seeks to move beyond conventional heuristic-based design flows by developing scalable learning-driven methods capable of reasoning about complex hardware designs.

His current research interests include foundation models for hardware design, AI-assisted RTL generation and verification, intelligent physical design and placement, GPU-accelerated Boolean satisfiability (SAT) solving and sampling, and structure-aware machine learning methods that operate directly on hardware representations, including logic circuits and LUT-based architectures.

The broader goal of his research is to develop AI-assisted hardware design workflows in which learning and reasoning models can automate and jointly optimize multiple stages of the chip design process—from specification and RTL generation to verification, synthesis, and physical implementation. This research aims to make hardware design more scalable, automated, and accessible while enabling the development of increasingly complex and efficient computing systems.

I am actively recruiting motivated graduate students (MSc and PhD) who are interested in AI-driven research at the intersection of artificial intelligence, computer hardware, and electronic design automation (EDA). Potential research topics include foundation models and generative AI for hardware design, AI-assisted RTL generation and verification, intelligent physical design, and machine learning for hardware optimization.

Students with backgrounds or interests in machine learning, computer architecture, digital hardware design, EDA, or related areas are encouraged to reach out. If you are interested in joining my research group at the University of Alberta, please contact me with your CV and a brief description of your research interests.