Deep Learning is affecting a change in EDA tools. Deep networks can accelerate analysis tools. Reinforcement learning and generative models can increase designer productivity and produce better designs. This talk will survey the application of deep learning to EDA tools and give an outlook for the future.
From its initial proposal, Quantum Computing (QC) has had captivating potential, and scientists have worked on advancing toward that potential. With well-known algorithms as motivation, and increasingly capable hardware devices, QC has now reached an interesting and important inflection point. The Algorithms-to-Devices gap in QC refers to the orders of magnitude difference between the quantity and quality of resources needed by QC algorithms, and what has been successfully built today. What is needed now are computer scientists and engineers to develop the crucial intermediate tool flows, abstraction layers, and programming languages that will help QC systems close this gap and reach practical quantum advantage. My talk will offer some recent results from my group about new applications, architecture, and design synthesis approaches for bridging the gap. More broadly, I will advocate for the role that computer scientists and engineers must play in order for QC to reach its full potential.
Ron Rohrer has held both academic and industrial positions during his career of over sixty years. He has been fortunate to have seen and, in some cases, to have impacted the EDA field since the 1960s. During that period he's observed that while some things have changed, some have stayed the same and some may be hindering more rapid progress. The talk will cover experiences, observations and insights garnered from academic and industry perspectives.
Over the past several decades, EDA innovation has been foundational to design progress. Rapid advances in design have, in turn, created computational capabilities that are now powering the AI revolution. In this talk, we discuss key advances that have brought us to this point of inflection, review state-of-the-art AI platform capabilities, and look at the exciting road ahead with AI advances powering EDA innovation that enables the next frontier of design.
