CPS Events
Mix & Latch: An Optimization Flow for High-Performance Designs with Single-Clock Mixed-Polarity Latches and Flip-Flops
Abstract
Flip-flops (FFs) are the most commonly used sequential elements in synchronous circuits, but their timing requirements limit the operating frequency. Borrowing time with a latch-based approach can increase operating frequency, but traditional back-end optimization tools struggle to manage hold time requirements. The Mix & Latch technique achieves higher frequencies and often lower area than commercial state-of-the-art retiming by exploiting four types of synchronous sequential gates, namely positive and negative edge-triggered FFs and positive and negative transparent latches, all using a single clock tree. The effectiveness of Mix & Latch is demonstrated on both standard logic synthesis benchmarks and on a RISC-V processor core from the Pulp platform using 28 nm CMOS FDSOI technology. The results are compared to retiming performed with a state-of-the-art tool, showing a 25 % frequency improvement over the traditional design flow and 7.5 % over the retiming flow. Compared to the retiming flow, we achieve comparable or lower power and area, while preserving the original registers and allowing logic equivalence checking.
Speaker Bio
Luciano Lavagno received his Ph.D. in EECS from U.C. Berkeley (California, USA) in 1992 and from Politecnico di Torino (Italy) in 1993. He co-authored two books on asynchronous circuit design, a book on hardware/software co-design of embedded systems, the CRC Handbook on Electronic Design Automation, and over 250 scientific papers. He has been granted 13 US patents. Between 1993 and 2000 he was the architect of the POLIS project, a cooperation between U.C. Berkeley, Cadence Design Systems, Magneti Marelli and Politecnico di Torino, which developed a complete hardware/software co-design environment for control-dominated embedded systems. Between 2003 and 2014 he has been one of the creators and architects of the Cadence C-to-Silicon high-level synthesis system. Between 2015 and 2017 he has worked, with the Calypto group of Mentor Graphics, on their high level synthesis tool, called Catapult. Since 2018 he has been leading the back-end team working on the Vitis HLS tool from Xilinx/AMD. Since 2011 he is also a full professor with Politecnico di Torino, Italy. Luciano has been serving for many years on the technical committees of the main international conferences in his field (e.g. DAC, DATE, ICCAD, ICCD, ASYNC, CODES) and of various workshops and symposia. He has been the technical program chair of DAC, and the TPC and general chair of CODES. A senior member of IEEE, Luciano has been also associate editor of IEEE TCAS and ACM TECS. His research interests include the high-level synthesis of digital circuits, in particular for acceleration of Machine Learning tasks, performance optimization of digital circuits, including asynchronous and Razor-like design techniques, as well as circuits and algorithms for indoor localization and tracking.
Hierarchical Contract Nets and Automatic Assurance Case Environment
Abstract:
An automatic synthesis problem is often characterized by an overall goal or specification to be satisfied, the set of all possible outcomes, called the design space, and an algorithm for the automatic selection of one or more members from the design space that are provably guaranteed to satisfy the overall specification. A key challenge in automatic synthesis is the complexity of the design space.
In the first half of the talk, we introduce a formal model, termed hierarchical contract nets (HCN), and a framework for the efficient automatic synthesis of hierarchical contract nets, based on a library of conditional refinement relations between contracts and contract nets. Assurance cases (ACs) have gained attention in the aerospace, medical, and other heavily-regulated industries as a means for providing structured arguments on why a product, typically a complex cyber-physical system, is dependable (i.e., safe, secure, etc.) for its intended application. Challenges in AC construction stem from the complexity, uniqueness and the heterogeneous nature of the CPS and the supporting evidence, and the need to assess the quality of the argument and evidence.
In the second half of the talk, we present an application of HCN in the DARPA program Automatic Rapid Certification of Software (ARCOS) for an automated AC creation framework that facilitates the synthesis, validation, and confidence assessment of ACs based on dependability argument patterns and confidence patterns capturing domain knowledge.
Speaker Bio:
Dr. Timothy E. Wang is currently at RTX Technologies Research Center (formerly Raytheon/United Technologies Research Center). He earned his B.S., M.S., and PhD all from the Department of Aerospace Engineering at Georgia Institute of Technology. He has been working on various aspects of the modeling, analysis, verification, and validation (V&V) and certification of complex cyber-physical systems. This includes application of formal methods to industrial systems such as Pratt & Whitney engine FADEC, compositional modeling and formal verification of human-machine systems, formal verification of on-board helicopter autonomy, and also machine learning with formal robustness guarantees. He has participated and led several government-sponsored research programs from DARPA, ONR and NASA.
Autonomy for Space Exploration
Abstract: Over the past two decades, several autonomous functions and system-level capabilities have successfully been demonstrated and used in deep-space operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models.
In this talk, Dr. Nesnas will share advances in the architecture, development, and deployment of autonomous systems for space applications, highlighting recent advances in entry descent and landing, rover navigation, and extreme terrain mobility. He will also describe progress toward future architecting of autonomous system and summarize anticipated needs based on recommendations from the Planetary Science and Astrobiology Decadal Survey.
Speaker Bio: Issa Nesnas is a principal technologist in the Autonomous Systems Division at the Jet Propulsion Laboratory, where he worked for over 25 years after several years in the robotics industry. He is currently an associate director of Caltech’s CAST (Center for Autonomous Systems and Technologies and JPL’s lead on NASA’s Capability Leadership Team for Autonomous Systems. At JPL, he led the Robotics Mobility and the Robotics Software Systems Groups across a span of thirteen years. His research included architectures for autonomous systems, perception-based navigation and manipulation, and extreme-terrain and microgravity mobility. He has served in multiple roles on three JPL rover missions. He is the recipient of the Magellan Award, JPL’s highest award for an individual scientific or technical accomplishment for his work on extreme terrain mobility.
Issa received a B.E. degree in Electrical Engineering from Manhattan College in 1991, and earned the M.S. and Ph.D. degrees in robotics from the Mechanical Engineering Department at the University of Notre Dame in 1993 and 1995 respectively.
Practical Control System Design via Emulated Industrial Experiments
Abstract
This talk will outline a novel approach to Control Education based on emulated industrial experiments. Several examples will be used as illustrations, including Cross Directional Control in Paper Machines, Continuous Casting Machines, Audio Compression, and Wind power Generation. The talk will be based on a forthcoming book, “Practical Control System Design: Real World Designs Implemented on Emulated Industrial Systems” by Medioli and Goodwin, John Wiley, and sons, to appear.
Speaker Bio
Graham Goodwin is an Emeritus Laureate Professor of Electrical Engineering at the University of Newcastle in Australia. His education includes B.Sc., B.E., and Ph.D. from the University of New South Wales. In 2010 he was awarded the IEEE Control Systems Field Award, in 2011 the ACA Wook Hyuan Kwon Education Award, and in 2013 he received the Rufus T. Oldenburger Medal from the American Society of Mechanical Engineers. He was twice awarded the International Federation of Automatic Control triennial Best Engineering Textbook Prize. In 2021 he was awarded the American Control Council John Ragazzini Education Award. He is a Fellow of IEEE; an Honorary Fellow of the Institute of Engineers, Australia; a Fellow of the International Federation of Automatic Control, a Fellow of the Australian Academy of Science; a Fellow of the Australian Academy of Technology, Science and Engineering; a Member of the International Statistical Institute; a Fellow of the Royal Society, London and a Foreign Member of the Royal Swedish Academy of Sciences. In 2021 he was recognized by the Australian Government by becoming an Officer in the General Division of the Order of Australia. He holds Honorary Doctorates from Lund Institute of Technology, Sweden and the Technion Israel. He is the co-author of eleven books, four edited books, and five hundred papers. He holds 16 International Patents covering rolling mill technology, telecommunications, mine planning, and mineral exploration. His current research interests include power electronics, boiler control systems, and management of type 1 diabetes.
Minkowski, Lyapunov, and Bellman: Inequalities and Equations for Stability and Optimal Control
Abstract:
The classical Lyapunov and Bellman equations, and inequalities, are cornerstone objects in linear systems theory. These equations, and inequalities, are concerned with convex quadratic functions verifying stability in cases of the Lyapunov equation and inequalities as well as optimality and stability in cases of the Bellman equation and inequalities. Rather peculiarly, prior to my work in the area, very little had been known about the related Lyapunov and Bellman equations, and inequalities, within the space of the Minkowski functions of nonempty convex compact subsets containing the origin in their interior. My recent research has provided complete characterizations of the solutions to the Lyapunov and Bellman equations, and inequalities, within the space of the Minkowski functions, referred to as the Minkowski–Lyapunov and Minkowski–Bellman equations, and inequalities, respectively. The talk reports key results underpinning the study of these fundamental equations and inequalities and their generalizations. The talk also renders strong evidence of topological flexibility and theoretical correctness of the developed frameworks and consequent advantages over the traditional Lyapunov and Bellman equations and inequalities.
Speaker Bio:
Saša V. Raković received the Ph.D. degree in Control Theory from Imperial College London. His Ph.D. research was awarded the Eryl Cadwaladr Davies Prize 2005 as the best PhD thesis in the EEE Department of Imperial College. Saša V. Raković has been affiliated with several well known international universities, including, inter alia, Imperial College London, ETH Zürich, Oxford University, UMD at College Park, UT at Austin and Texas A&M University at College Station. He is currently a full Professor at Beijing Institute of Technology, Beijing. His research interests and activity span broad areas of artificial intelligence, autonomy, controls, dynamics, systems, applied mathematics, optimization, and set-valued analysis. His research is driven by mathematical problems that belong to the intersection of artificial intelligence, controls, dynamics, systems, and optimization. Saša V. Raković has, together with William S. Levine, edited a well-received “Handbook of Model Predictive Control” published by Springer Nature. Saša V. Raković has authored 120 publications, most of which are highly cited (i.e., about 8000 citations according to Google Scholar) and are published in leading international journals and proceedings of key conferences in the fields of control theory and engineering. Saša V. Raković’s research has made contributions to theory, computation, and implementation of conventional, robust and stochastic model predictive control, and he is best known as one of the key contributors to the tube model predictive control framework.
