Democratizing IC Design and Customized Computing
UCLA Computer Science Department
Director, Center for Domain-Specific Computing (CDSC)
The electronic design automation (EDA) traditionally serves the hardware design community. As we enter the era of customized computing with increasing amount of computation moves from general-purpose CPUs to domain-specific accelerators (DSAs) due to their performance and energy efficiency, there is a pressing need and opportunity for the EDA community to empower millions of software programmers to create their own DSAs to accelerate the compute-intensive portion of their applications. High-level synthesis (HLS) made an important progress in this direction, but far from sufficient. In this talk, I shall discuss the recent progresses in this area, including microarchitecture guided optimization, such as automated systolic array generation, automated source-to-source transformation based on graph-based neural networks and meta learning, and support of domain-specific languages widely used in the software community. I hope to see the ICCAD community in joining the effort of broadening the participation of IC designs and customized computing.
Jason Cong is the Volgenau Chair for Engineering Excellence Professor (and former Department Chair) at the UCLA Computer Science Department, with joint appointment from the Electrical Engineering Department, the director of Center for Domain-Specific Computing (CDSC), and the director of VLSI Architecture, Synthesis, and Technology (VAST) Laboratory. Dr. Cong’s research interests include electronic design automation, novel architectures and compilation for customizable computing, and quantum computing. He has close to 500 publications in these areas, including 16 best paper awards, three 10-Year Most Influential Paper Awards, and three papers inducted to the FPGA and Reconfigurable Computing Hall of Fame. He and his former students co-founded AutoESL and Falcon Computing Solutions. Both were acquired by Xilinx and led to the most widely used high-level synthesis tool for FPGAs. He was elected to an IEEE Fellow in 2000, ACM Fellow in 2008, and the National Academy of Engineering in 2017. He is the recipient of the 2022 IEEE Robert Noyce Medal for fundamental contributions to electronic design automation and FPGA design methods.
Automated Cryptographically-Secure Private Computing: From Logic and Mixed-Protocol Optimization to Centralized and Federated ML Customization
Henry Booker Scholar Professor of ECE at the University of California San Diego (UCSD)
Over the last four decades, much research effort has been dedicated to designing cryptographically-secure methods for computing on encrypted data. However, despite the great progress in research, adoption of the sophisticated crypto methodologies has been rather slow and limited in practical settings. Presently used heuristic and trusted third party solutions fall short in guaranteeing the privacy requirements for the contemporary massive datasets, complex AI algorithms, and the emerging collaborative/distributed computing scenarios such as blockchains.
In this talk, we outline the challenges in the state-of-the-art protocols for computing on encrypted data with an emphasis on the emerging centralized, federated, and distributed learning scenarios. We discuss how in recent years, giant strides have been made in this field by leveraging optimization and design automation methods including logic synthesis, protocol selection, and automated co-design/co-optimization of cryptographic protocols, learning algorithm, software, and hardware. Proof of concept would be demonstrated in the design of COINN, the present state-of-the-art framework for cryptographically-secure deep learning on encrypted data. We conclude by discussing the practical challenges in the emerging private robust learning and distributed/ federated computing scenarios as well as the opportunities ahead.
Farinaz Koushanfar is the Henry Booker Scholar Professor of ECE at the University of California San Diego (UCSD), where she is also the founding co-director of the UCSD Center for Machine-Intelligence, Computing & Security (MICS). Her research addresses several aspects of secure and efficient computing, with a focus on hardware and system security, robust machine learning under resource constraints, intellectual property (IP) protection, as well as practical privacy-preserving computing. Dr. Koushanfar is a fellow of the Kavli Frontiers of the National Academy of Sciences and a fellow of IEEE. She has received a number of awards and honors including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama, the ACM SIGDA Outstanding New Faculty Award, Cisco IoT Security Grand Challenge Award, MIT Technology Review TR-35, Qualcomm Innovation Awards, Intel Collaborative Awards, Young Faculty/CAREER Awards from NSF, DARPA, ONR and ARO, as well as several best paper awards.