Supervised Design-Space Exploration

Low-cost Very Large Scale Integration (VLSI) electronics have revolutionized daily life and expanded the role of computation in science and engineering. Meanwhile, process-technology scaling has changed VLSI design to an exploration process that strives for the optimal balance among multiple objectives, such as power, performance, and area, i.e. multi-objective Pareto-set optimization. Besides, modern VLSI design has shifted to synthesis-centric methodologies in order to boost the design productivity, which leads to better design quality given limited time and resources. However, current decade-old synthesis-centric design methodologies suffer from: (i) long synthesis tool runtime, (ii) elusive optimal setting of many synthesis knobs, (iii) limitation to one design implementation per synthesis run, and (iv) limited capability of digesting only component-level designs as opposed to holistic system-wide synthesis. These challenges make Design Space Exploration (DSE) with synthesis tools a daunting task for both novice and experienced VLSI designers, thus stagnating the development of more powerful (i.e. more complex) computer systems. To address these challenges, I propose Supervised Design-Space Exploration (SDSE), an abstraction layer between a designer and a synthesis tool, aiming to autonomously supervise synthesis jobs for DSE. For system-level exploration, SDSE can approximate a system Pareto set given limited information: only lightweight component characterization is required, yet the necessary component synthesis jobs are discovered on-the-fly in order to compose the system Pareto set. For component-level exploration, SDSE can approximate a component Pareto set by iteratively refining the approximation with promising knob settings, guided by synthesis-result estimation with machine-learning models. Combined, SDSE has been applied with the three major synthesis stages, namely high-level, logic, and physical synthesis, to the design of heterogeneous accelerator cores as well as high-performance processor cores. In particular, SDSE has been successfully integrated into the IBM Synthesis Tuning System, yielding 20% better circuit performance than the original system on the design of a 22nm server processor that is currently in production. Looking ahead, SDSE can be applied to other VLSI designs beyond the accelerator and the programmable cores. Moreover, SDSE opens several research avenues for: (i) new development and deployment platforms of synthesis tools, (ii) large-scale collaborative design engineering, and (iii) new computer-aided design approaches for new classes of systems beyond VLSI chips

Information Bottleneck

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence

Model-based symbolic design space exploration at the electronic system level: a systematic approach

In this thesis, a novel, fully systematic approach is proposed that addresses the automated design space exploration at the electronic system level. The problem is formulated as multi-objective optimization problem and is encoded symbolically using Answer Set Programming (ASP). Several specialized solvers are tightly coupled as background theories with the foreground ASP solver under the ASP modulo Theories (ASPmT) paradigm. By utilizing the ASPmT paradigm, the search is executed entirely systematically and the disparate synthesis steps can be coupled to explore the search space effectively.In dieser Arbeit wird ein vollständig systematischer Ansatz präsentiert, der sich mit der Entwurfsraumexploration auf der elektronischen Systemebene befasst. Das Problem wird als multikriterielles Optimierungsproblem formuliert und symbolisch mit Hilfe von Answer Set Programming (ASP) kodiert. Spezialisierte Solver sind im Rahmen des ASP modulo Theories (ASPmT) Paradigmas als Hintergrundtheorien eng mit dem ASP Solver gekoppelt. Durch die Verwendung von ASPmT wird die Suche systematisch ausgeführt und die individuellen Schritte können gekoppelt werden, um den Suchraum effektiv zu durchsuchen

Evolutionary Reinforcement Learning: A Survey

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, there remain several crucial challenges, including brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, especially in continuous search space scenarios, difficulties in credit assignment in multi-agent reinforcement learning, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research fields in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century