ML-Based Techniques for Physical Design Automation of SoCs

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Semiconductors and the integrated circuits (ICs) made from them are ubiquitous and continue to penetrate all sectors of our economies, all built and natural environments, the globe, and outer space. ICs built with state of the art manufacturing technologies contain billions of electronic devices (transistors) interconnected together through myriads of intricate and complex layers and strips (wires) of conducting and non-conducting materials. Completing the design and verifying the correctness of such an interconnection network presents formidable challenges. These challenges are partly overcome through the aid of sophisticated computer-aided design (CAD) or electronic design automation (EDA) tools. These tools, in turn, amount to massive, highly complex software packages used by teams of integrated circuit designers worldwide. The level of complexity of the integrated circuits of today and those envisioned in the near and longer term is such that the EDA tools, while able to provide invaluable aid, tend to be very slow to yield viable solutions as the computational complexity of the problem at hand tends to be extremely large. This research is focused on developing novel methodologies aimed at significantly accelerating the phases of integrated circuit design commonly referred to as physical IC design and more specifically as "place and route". We propose to develop a new framework that draws on artificial intelligence, and, in particular, machine learning techniques, to accelerate and improve the quality of place and route EDA tools. The improvements and acceleration we aim for will allow IC designers' efforts to converge on better designs faster. Overall, this paves the way to the production of higher performance ICs in lesser time and at reduced cost. Many Canadian and multi-national companies and organizations are in the business of developing ICs for applications in various sectors. Our research outcomes would directly benefit such companies. Many other organizations, Canadian and multi-national operating in Canada, have their businesses directly or indirectly dependent on ICs. These organizations will also benefit, albeit indirectly, from the results of the work proposed here.