Giovanni De Micheli

Bio: Giovanni De Micheli is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Logic gate & Logic synthesis. The author has an hindex of 55, co-authored 607 publications receiving 14895 citations. Previous affiliations of Giovanni De Micheli include École Normale Supérieure & Stanford University.

Synthesis and optimization of digital circuits

Abstract: From the Publisher: Synthesis and Optimization of Digital Circuits offers a modern, up-to-date look at computer-aided design (CAD) of very large-scale integration (VLSI) circuits. In particular, this book covers techniques for synthesis and optimization of digital circuits at the architectural and logic levels, i.e., the generation of performance-and/or area-optimal circuits representations from models in hardware description languages. The book provides a thorough explanation of synthesis and optimization algorithms accompanied by a sound mathematical formulation and a unified notation. The text covers the following topics: modern hardware description languages (e.g., VHDL, Verilog); architectural-level synthesis of data flow and control units, including algorithms for scheduling and resource binding; combinational logic optimization algorithms for two-level and multiple-level circuits; sequential logic optimization methods; and library binding techniques, including those applicable to FPGAs.

Networks on Chips: Technology and Tools

Abstract: The design of today's semiconductor chips for various applications, such as telecommunications, poses various challenges due to the complexity of these systems. These highly complex systems-on-chips demand new approaches to connect and manage the communication between on-chip processing and storage components and networks on chips (NoCs) provide a powerful solution. This book is the first to provide a unified overview of NoC technology. It includes in-depth analysis of all the on-chip communication challenges, from physical wiring implementation up to software architecture, and a complete classification of their various Network-on-Chip approaches and solutions.

System-level power optimization: techniques and tools

Abstract: This tutorial surveys design methods for energy-efficient system-level design. We consider electronic sytems consisting of a hardware platform and software layers. We consider the three major constituents of hardware that consume energy, namely computation, communication, and storage units, and we review methods of reducing their energy consumption. We also study models for analyzing the energy cost of software, and methods for energy-efficient software design and compilation. This survery is organized around three main phases of a system design: conceptualization and modeling design and implementation, and runtime management. For each phase, we review recent techniques for energy-efficient design of both hardware and software.

Analysis of power consumption on switch fabrics in network routers

Abstract: In this paper, we introduce a framework to estimate the power consumption on switch fabrics in network routers. We propose different modeling methodologies for node switches, internal buffers and interconnect wires inside switch fabric architectures. A simulation platform is also implemented to trace the dynamic power consumption with bit-level accuracy. Using this framework, four switch fabric architectures are analyzed under different traffic throughput and different numbers of ingress/egress ports. This framework and analysis can be applied to the architectural exploration for low power high performance network router designs.

Dynamic Power Management: Design Techniques and CAD Tools

Abstract: From the Publisher: Dynamic Power Management: Design Techniques and CAD Tools addresses design techniques and computer-aided design solutions for power management. Different approaches are presented and organized in an order related to their applicability to control-units, macro-blocks, digital circuits and electronic systems, respectively. All approaches are based on the principle of exploiting idleness of circuits, systems, or portions thereof. They involve both detection of idleness conditions and the freezing of power-consuming activities in the idle components. Dynamic Power Management: Design Techniques and CAD Tools is of interest to researchers and developers of computer-aided design tools for integrated circuits and systems, as well as to system designers.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.