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 …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Convex Analysisの二,三の進展について

Preface. 1. Introduction J.M. Rabaey, et al. Part I: Technology and circuit design levels. 2. Device and technology impact on low power electronics Chenming Hu. 3. Low power circuit technologies C. Svensson, Dake Liu. 4. Energy-recovery CMOS W.C. Athas. 5. Low power clock distribution J.G. Xi, W.W.-M. Dai. Part II: Logic and module design levels. 6. Logic synthesis and module design levels M. Pedram. 7. Low power arithmetic components T.K. Callawy, E.E. Schwartzlander. 8. Low power memory design K. Itoh. Part III: Architecture and system design levels. 9. Low-power microprocessor design S. Gary. 10. Portable video-on-demand in wireless communication T.H. Meng, et al. 11. Algorithm and architectural level methodologies R. Mehra, et al. Index.

Low Power Design Methodologies

Low power has emerged as a principal theme in today's electronics industry. The need for low power has caused a major paradigm shift in which power dissipation is as important as performance and area. This article presents an in-depth survey of CAD methodologies and techniques for designing low power digital CMOS circuits and systems and describes the many issues facing designers at architectural, logical, and physical levels of design abstraction. It reviews some of the techniques and tools that have been proposed to overcome these difficulties and outlines the future challenges that must be met to design low power, high performance systems.

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Power minimization in IC design: principles and applications

The growing packing density and power consumption of very large scale integration (VLSI) circuits have made thermal effects one of the most important concerns of VLSI designers The increasing variability of key process parameters in nanometer CMOS technologies has resulted in larger impact of the substrate and metal line temperatures on the reliability and performance of the devices and interconnections Recent data shows that more than 50% of all integrated circuit failures are related to thermal issues This paper presents a brief discussion of key sources of power dissipation and their temperature relation in CMOS VLSI circuits, and techniques for full-chip temperature calculation with special attention to its implications on the design of high-performance, low-power VLSI circuits The paper is concluded with an overview of techniques to improve the full-chip thermal integrity by means of off-chip versus on-chip and static versus adaptive methods

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Thermal Modeling, Analysis, and Management in VLSI Circuits: Principles and Methods

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both nonpipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level (selected from a fixed and known number of voltage levels) to each operation in a data flow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using three supply voltage levels on a number of standard benchmarks, an average energy saving of 40.19% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using a single supply voltage level.

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Energy minimization using multiple supply voltages

The first part of this paper describes two runtime mechanisms for reducing the leakage current of a CMOS circuit. In both cases, it is assumed that the system or environment produces a "sleep" signal that can be used to indicate that the circuit is in a standby mode. In the first method, the "sleep" signal is used to shift in a new set of external inputs and pre-selected internal signals into the circuit with the goal of setting the logic values of all of the internal signals so as to minimize the total leakage current in the circuit. This minimization is possible because the leakage current of a CMOS gate is strongly dependent on the input combination applied to its inputs. In the second method, nMOS and pMOS transistors are added to some of the gates in the circuit to increase the controllability of the internal signals of the circuit and decrease the leakage current of the gates using the "stack effect". This is, however, done carefully so that the minimum leakage is achieved subject to a delay constraint for all input-output paths in the circuit. In both cases, Boolean satisfiability is used to formulate the problems, which are subsequently solved by employing a highly efficient SAT solver. Experimental results on the combinational circuits in the MCNC91 benchmark suite demonstrate that it is possible to reduce the leakage current in combinational circuits by an average of 25% with only a 5% delay penalty. The second part of this paper presents a design technique for applying the minimum leakage input to a sequential circuit. The proposed method uses the built-in scan-chains in a VLSI circuit to drive it with the minimum leakage vector when it enters the sleep mode. The use of these scan registers eliminates the area and delay overhead of the additional circuitry that would otherwise be needed to apply the minimum leakage vector to the circuit. Experimental results on the sequential circuits in the MCNC91 benchmark suit show that, by using the proposed method, it is possible to reduce the leakage by an average of 25% with practically no delay penalty.

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Massoud Pedram

Papers

Low Power Design Methodologies

Power minimization in IC design: principles and applications

Thermal Modeling, Analysis, and Management in VLSI Circuits: Principles and Methods

Energy minimization using multiple supply voltages

Leakage current reduction in CMOS VLSI circuits by input vector control