Phoenix, Arizona, USA: Professor Vidya Chhabria, an Indian-origin researcher, has been honoured with Google’s ML & Systems Junior Faculty Award for her groundbreaking work in AI-powered chip design automation.
Chhabria, who is an assistant professor at Arizona State University's School of Electrical, Computer, and Energy Engineering, has received $100,000 in unrestricted funding through this award. The recognition highlights her contributions to advancing artificial intelligence applications in very large-scale integration (VLSI) system design.
Her research focuses on addressing fundamental challenges in physical design, including optimising for speed, power consumption, area efficiency, and manufacturability—areas where conventional tools often fall short.
A significant aspect of Chhabria’s work involves creating AI-enhanced electronic design automation (EDA) tools. This includes autonomous AI agents capable of handling complex layout challenges and generative approaches that produce synthetic, large-scale datasets to compensate for the scarcity of industrial data. These innovations are designed to make chip development faster, more scalable, and widely accessible.
Training AI systems for chip design is particularly difficult because there is limited access to open-source, industry-scale chip layouts, making it hard to train and validate models, Chhabria explained in a statement to the university press.
The Google award helps overcome these obstacles by enabling collaboration with the tech giant. Chhabria noted that mentorship, industry insights, and potential access to computational resources will ensure her work remains relevant and continues to push forward AI applications in semiconductor design.
Beyond funding, the award fosters collaboration between academia and industry. ASU leaders highlighted that receiving recognition from Google reflects the significance and influence of Chhabria’s research.
Looking ahead, Chhabria envisions these developments as crucial for next-generation hardware, powering technologies from large-scale data centres to edge devices. As chip systems become increasingly complex, her work positions Arizona State University at the forefront of integrating AI-based EDA tools into the future of semiconductor innovation.
