EDAML 2022 Invited Speaker 5: Combining Optimization and Machine Learning in Physical Design

Abstract

The exponential increase in computing power and the availability of big data have ignited innovations in EDA. The most recent trend in innovations has involved using machine learning algorithms for solving problems of scale. Machine learning techniques can solve large-scale problems efficiently once they are trained. However, their training takes a large amount of computing power and might not translate well from one type of problem to another. On the other hand, many of the existing algorithms in physical design take advantage of mathematical optimization techniques to improve their solution quality. These techniques can find optimal or near-optimal solutions using fast heuristics. These techniques do not require a large amount of data but need some level of insight into the nature of the problem by the designer. The mathematical optimization techniques rely heavily on the developed models. In this talk, we will discuss how machine learning can be used to develop better models for optimization problems and how optimization techniques can then use the models to generate more data to improve the accuracy and robustness of machine learning techniques. We will first discuss the algorithm-driven nature of the optimization techniques and compare that to the data-driven nature of the machine learning techniques. We will use examples of physical design placement and routing. Then, we will discuss how optimization and ML can be used to solve the problems of scale both in numbers and transistor sizes. We will also discuss how reinforcement learning can be used to come up with new heuristics for solving the problems encountered in physical design. The talk will end with some practical suggestions on how to improve the quality and speed of the design.