Convergence of SoC architecture and semiconductor manufacturing through AI/ML systems
Special Session (Research Track)
Hosted in Virtual Platform
DescriptionWith the rising needs of advanced algorithms for large-scale data analysis and data-driven discovery, and significant growth in emerging applications from the edge to the cloud, we need low-cost, high-performance, energy-efficient, and reliable computing systems targeted for these applications. Developing these application-specific hardware elements must become easy, inexpensive, and seamless to keep up with extremely rapid evolution of AI/ML algorithms and applications. Therefore, it is of high priority to create innovative design frameworks enabled by data analytics and machine learning that reduces the engineering cost and design time of application-specific hardware. There is also a need to continually advance software algorithms and frameworks to better cope with data available to platforms at multiple scales of complexity. To the best of our knowledge, this is the first special session at any EDA conference that explores both directions of cross-fertilization between computing system design and ML.