Wednesday, December 15, 2021

  • TILOS Seminar Series: "Closing the Virtuous Cycle of AI for IC and IC for AI" Presenter: David Pan, Professor, University of Texas at Austin

Abstract: The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms, big-data, and computing power enabled by modern integrated circuits (ICs), including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two main themes, AI for IC and IC for AI. As semiconductor technology enters the era of extreme scaling and heterogeneous integration, IC design and manufacturing complexities become extremely high. More intelligent and agile IC design technologies are needed than ever to optimize performance, power, manufacturability, design cost, etc., and deliver equivalent scaling to Moore’s Law. This talk will present some recent results leveraging modern AI and machine learning advancement with domain-specific customizations for agile IC design and manufacturing, including open-sourced DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards), DARPA-funded MAGICAL project for analog IC design automation, and LithoGAN for design-technology co-optimization. Meanwhile on the IC for AI frontier, customized ICs, including those with beyond-CMOS technologies, can drastically improve AI performance and energy efficiency by orders of magnitude. I will present our recent results on hardware and software co-design for optical neural networks and photonic ICs (which won the 2021 ACM Student Research Competition Grand Finals 1st Place). Closing the virtuous cycle between AI and IC holds great potential to significantly advance the state-of-the-art of each other.

Tuesday, December 7, 2021

  • Designer, IP and Embedded Systems Track Presentation at the 58th Design Automation Conference: "Exchanging EDA data for AI/ML using Standard API" Presenters: Kerim Kalafala (IBM), Lakshmanan Balasubramanian (Texas Instruments), Firas Mohammed (Silvaco), Andrew B. Kahng (UC San Diego). (Link)

Monday, December 6, 2021

  • Tutorial at the 58th Design Automation Conference: "Adding machine learning to the mix of EDA optimization algorithms" Presenters: Ismail Bustany (Xilinx), Andrew B. Kahng (UC San Diego), Padmani Gopalakrishnan (Xilinx). (Link)

Wednesday, November 17, 2021

  • TILOS Seminar Series: "A Mixture of Past, Present, and Future" Presenter: Arya Mazumdar

Abstract: The problems of heterogeneity pose major challenges in extracting meaningful information from data as well as in the subsequent decision making or prediction tasks. Heterogeneity brings forward some very fundamental theoretical questions of machine learning. For unsupervised learning, a standard technique is the use of mixture models for statistical inference. However for supervised learning, labels can be generated via a mixture of functional relationships. We will provide a survey of results on parameter learning in mixture models, some unexpected connections with other problems, and some interesting future directions.

Tuesday, November 2, 2021

  • "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD" paper presentation in Session 7C of ICCAD-2021. (Link)

Saturday, October 16, 2021

Friday, October 15, 2021

  • Andrew B. Kahng (UC San Diego) spoke in the "Industry" segment of the NSF Integrated Circuits Research, Education, and Workforce Development Workshop. (Link)