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

In this paper, we present an accurate approach for the estimation of statistical distribution of leakage power consumption in the presence of process variations in nano-scale CMOS technologies. The technique, which is additive with respect to the individual gate leakage values, employs Generalized Extreme Value (GEV) distribution. Compared to the previous methods based on (two-parameter) lognormal distribution, this method uses GEV distribution with three parameters to increase the accuracy. Using the suggested distribution, the leakage yield of circuits may be modeled. The accuracy of the approach is studied by comparing its results with those of a previous technique and HSPICE-based Monte Carlo simulations on ISCAS85 benchmark circuits for 45 nm CMOS technology. The comparison reveals a higher accuracy for the proposed approach. The proposed distribution does not add to the complexity and cost of simulations compared to the case of the lognormal distribution.

Statistical Estimation of Leakage Power Dissipation in Nano-Scale CMOS Digital Circuits using Generalized Extreme Value Distribution

Field Programmable gate arrays (FPGAs) are one of the most successful circuits in the semiconductor industry. In the absence of a reliable three-terminal switch like MOSFET for rapid single flux quantum(RSFQ) technology, it was difficult to implement FPGA like reconfiguralbe circuits. However, a recently proposed superconducting magnetic FPGA (SMFPGA) implements a controllable switch by controlling the critical current of magnetic Josephson Junctions (MJJs) placed in energy-efficient RSFQ bias network. For implementing a configurable logic block (CLB) with a smaller area for the said FPGA, we designed a reconfigurable gate that can implement four basic logical functions: AND, OR, XOR and NOT. The programmability to implement the four functions is achieved by introducing MJJs in the circuit at specific locations and programming their critical current. The gate is made reconfigurable by having the ability to change both the bias current at different ports and the critical current of a JJ in the gate to two different values. This makes the size of CLB ten times smaller compared to the earlier design and simplifies the SMFPGA. We describe the design methodology and the simulation results of the reconfigurable gate.

Reconfigurable Logic Cell for Superconducting Magnetic Field Programmable Gate Array

This paper presents the design of a one-bit full adder with sum and carry outputs as two single-stage gates, which could save the JJ count and the area compared with the conventional design of a full-adder. The schematics of both the Sum and Carry cells are shown in this paper along with their input and output waveforms of JSIM simulations in the time domain. The circuit cost is compared between the conventional full adder and the new single-stage adder design in terms of the area and the time. Integer dividers of several sizes are synthesized with conventional full-adder and the proposed single-stage adder to illustrate the advantages of the new design.

Design of an SFQ Full Adder as a Single-Stage Gate

Mobile and portable information systems are pushing electronics and the development process In their wake are new requirements that drive low-power design This is however by no means the only driving force behind the need for a dramatic reduction of power dissipation in digital ICs There also exists a strong demand on producers of high end products to reduce power consumption Power minimization is thus vital for different reasons in different applications Design knowledge and experience in minimizing power while optimizing performance is very limited Low-power design is in its infancy The same can be said for design tools The opportunities for ultra low power tools and methodologies that achieve dramatic reduction of power and push higher up the design entry point are all around us However, the need for tools that broaden the low-power design envelope is not being articulated by the user community very strongly This panel seeks to expose solutions that designers and EDA vendors have, to present proper vehicles for transferring the low power design techniques and methodologies to the user community, and to provide a forum for exploring what is still needed What design paradigms make sense, what levels of accuracy and speed are acceptable to the users and what tools have the highest payoff are part of what this panel is all about

Panel: Power Minimization in IC Design

In this paper, we present an error estimation and propagation technique which targets high-level design abstraction through considering data-flow graph (DFG) representation of approximate circuits. The proposed technique is utilized for pruning different combinations of exact versus approximate realizations of various operations in the DFG for a design space exploration (DSE) framework. The technique relies on the output error estimation of the arithmetic modules of a high-level library by dividing the input range to intervals and then considering different combinations of these intervals (cluster-based estimation of output error). Additionally, the estimation considers the error of the operands. The error for each combination is stored in a look up table (LUT). The efficacy of the proposed method is assessed for two image processing applications. Simulation results show that the framework can efficiently generate the Pareto frontier in the trade-off space of accuracy versus energy efficiency for the two applications.

Massoud Pedram

Papers

Statistical Estimation of Leakage Power Dissipation in Nano-Scale CMOS Digital Circuits using Generalized Extreme Value Distribution

Reconfigurable Logic Cell for Superconducting Magnetic Field Programmable Gate Array

Design of an SFQ Full Adder as a Single-Stage Gate

Panel: Power Minimization in IC Design

An Efficient Error Estimation Technique for Pruning Approximate Data-Flow Graphs in Design Space Exploration