TILOS Seminar: Machine Learning for Design Methodology and EDA Optimization
Haoxing Ren, NVIDIA
In this talk, I will first illustrate how ML helps improve design quality as well as design productivity from design methodology perspective with examples in digital and analog designs. Then I will discuss the potential of applying ML to solve challenging EDA optimization problems, focusing on three promising ML techniques: reinforcement learning (RL), physics-based modeling and self-supervised learning (SSL). RL learns to optimize the problem by converting the EDA problem objectives into environment rewards. It can be applied to both directly solve the EDA problem or be part of a conventional EDA algorithm. Physics-based modeling enables more accurate and transferable learning for EDA problems. SSL learns the optimized EDA solution data manifold. Conditioned on the problem input, it can directly produce the solution. I will illustrate the applications of these techniques in standard cell layout, computational lithography, and gate sizing problems. Finally, I will outline three main approaches to integrate ML and conventional EDA algorithms together and the importance of adopting GPU computing to EDA.
Haoxing Ren (Mark) leads the Design Automation research group at NVIDIA Research. His research interests are machine learning applications in design automation and GPU accelerated EDA. Before joining NVIDIA in 2016, he spent 15 years at IBM Microelectronics and IBM Research working on physical design and logic synthesis tools and methodologies for IBM microprocessor and ASIC designs. He received several IBM technical achievement awards including the IBM Corporate Award for his work on improving microprocessor design productivity. He published many papers in the field of design automation including several book chapters in logic synthesis and physical design. He also received the best paper awards at ISPD’2013, DAC’2019 and TCAD’2021. He earned his PhD in Computer Engineering from University of Texas at Austin in 2006.
TILOS Seminar: How to use Machine Learning for Combinatorial Optimization
Sherief Reda, Professor, Brown University and Principal Research Scientist at Amazon
Combinatorial optimization methods are routinely used in many scientific fields to identify optimal solutions among a large but finite set of possible solutions for problems of interests. Given the recent success of machine learning techniques in classification of natural signals (e.g., voice, image, text), it is natural to ask how machine learning methods can be used to improve the quality of solution or the runtime of combinatorial optimization algorithms? In this talk I will provide a general taxonomy and research directions for the use of machine learning techniques in combinatorial optimization. I will illustrate these directions using a number of case studies from my group's research, which include (1) improving the quality of results of integer linear programming (ILP) solver using deep metric learning, and (2) using reinforcement learning techniques to optimize the size of graphs arising in digital circuit design.
Sherief Reda is a Full Professor at the School of Engineering and Computer Science Department at Brown University and a Principal Research Scientist at Amazon. He joined Brown University in 2006 after receiving his Ph.D. in computer science and engineering from University of California, San Diego. He has over 135 research articles in the areas of energy-efficient computing, electronic design automation and combinatorial optimization, as well as several patents. Professor Reda received a number of research acknowledgments and awards, including eight best paper nominations, three best paper awards, and a National Science Foundation CAREER award. He has been a PI or co-PI on more than $21.1M of funded projects from federal agencies and industry corporations. He is a senior member of IEEE.
The FPGA Physical Design Flow Through the Eyes of Machine Learning
Dr. Ismail Bustany, Fellow, AMD
The FPGA physical design (PD) flow has innate features that differentiate it from its sibling, the ASIC PD flow. FPGA device families service a wide range of applications, have much longer lifespans in production use, and bring templatized logic layout and routing interconnect fabrics whose characteristics are captured by detailed device models and simpler timing and routing models (e.g. buffered interconnect and abstracted routing resources). Furthermore, the FPGA PD flow is a “one-stop shop” from synthesis to bitstream generation. This avails complete access to annotate, instrument, and harvest netlist and design features. These key differences provide rich opportunities to exploit both device data and design application specific contexts in optimizing various components of the PD flow. In this talk, I will present examples for the application of ML in device modeling and parameter optimization, draw attention to exciting research opportunities for applying the “learning to optimize” paradigm to solving the placement and routing problems, and share some practical learnings.
Dr. Ismail Bustany is a Fellow at AMD, where he works on physical design implementation and MLCAD . He has served on the technical program committees for the ISPD, ISQED, and DAC. He was the 2019 ISPD general chair. He currently serves on the organizing committees for the ICCAD and SLIP. He organized the 2014 and 2015 ISPD detailed routing-driven placement contests and co-organized the 2017 ICCAD detailed placement contest. His research interests include physical design, computationally efficient optimization algorithms, MLCAD, sparse matrix computations/acceleration, and partitioning algorithms. He earned his B.S. in CSE from UC San Diego and M.S./Ph.D. in EECS from UC Berkeley.
TILOS Seminar: Learning in the Presence of Distribution Shifts: How does the Geometry of Perturbations Play a Role?
Hamed Hassani, Assistant Professor, University of Pennsylvania
In this talk, we will focus on the emerging field of (adversarially) robust machine learning. The talk will be self-contained and no particular background on robust learning will be needed. Recent progress in this field has been accelerated by the observation that despite unprecedented performance on clean data, modern learning models remain fragile to seemingly innocuous changes such as small, norm-bounded additive perturbations. Moreover, recent work in this field has looked beyond norm-bounded perturbations and has revealed that various other types of distributional shifts in the data can significantly degrade performance. However, in general our understanding of such shifts is in its infancy and several key questions remain unaddressed.
The goal of this talk is to explain why robust learning paradigms have to be designed—and sometimes rethought—based on the geometry of the input perturbations. We will cover a wide range of perturbation geometries from simple norm-bounded perturbations, to sparse, natural, and more general distribution shifts. As we will show, the geometry of the perturbations necessitates fundamental modifications to the learning procedure as well as the architecture in order to ensure robustness. In the first part of the talk, we will discuss our recent theoretical results on robust learning with respect to various geometries, along with fundamental tradeoffs between robustness and accuracy, phase transitions, etc. The remaining portion of the talk will be about developing practical robust training algorithms and evaluating the resulting (robust) deep networks against state-of-the-art methods on naturally-varying, real-world datasets.
TILOS Seminar: The Connections Between Discrete Geometric Mechanics, Information Geometry, Accelerated Optimization and Machine Learning
Melvin Leok, Professor of Mathematics, UC San Diego
Geometric mechanics describes Lagrangian and Hamiltonian mechanics geometrically, and information geometry formulates statistical estimation, inference, and machine learning in terms of geometry. A divergence function is an asymmetric distance between two probability densities that induces differential geometric structures and yields efficient machine learning algorithms that minimize the duality gap. The connection between information geometry and geometric mechanics will yield a unified treatment of machine learning and structure-preserving discretizations. In particular, the divergence function of information geometry can be viewed as a discrete Lagrangian, which is a generating function of a symplectic map, that arise in discrete variational mechanics. This identification allows the methods of backward error analysis to be applied, and the symplectic map generated by a divergence function can be associated with the exact time-h flow map of a Hamiltonian system on the space of probability distributions. We will also discuss how time-adaptive Hamiltonian variational integrators can be used to discretize the Bregman Hamiltonian, whose flow generalizes the differential equation that describes the dynamics of the Nesterov accelerated gradient descent method.
Melvin Leok is professor of mathematics and co-director of the CSME graduate program at the UC San Diego. His research interests are in computational geometric mechanics, computational geometric control theory, discrete geometry, and structure-preserving numerical schemes, and particularly how these subjects relate to systems with symmetry. He received his Ph.D. in 2004 from the California Institute of Technology in Control and Dynamical Systems under the direction of Jerrold Marsden. He is a three-time NAS Kavli Frontiers of Science Fellow, a Simons Fellow in Mathematics, and has received the DoD Newton Award for Transformative Ideas, the NSF Faculty Early Career Development (CAREER) award, the SciCADE New Talent Prize, the SIAM Student Paper Prize, and the Leslie Fox Prize (second prize) in Numerical Analysis. He has given plenary talks at Foundations of Computational Mathematics, NUMDIFF, and the IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control. He serves on the editorial boards of the Journal of Nonlinear Science, the Journal of Geometric Mechanics, and the Journal of Computational Dynamics, and has served on the editorial boards of the SIAM Journal on Control and Optimization, and the LMS Journal of Computation and Mathematics.
TILOS Seminar: Closing the Virtuous Cycle of AI for IC and IC for AI
David Pan, Professor, University of Texas at Austin
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.
