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

This paper presents a minimum area, low-power driven clustering algorithm for coarse-grained, antifuse-based FPGAs under delay constraints. The algorithm accurately predicts logic replication caused by timing constraint during the low-power driven clustering. This technique reduces size of duplicated logic substantially, resulting in benefits in area, delay, and power dissipation. First, we build power-delay curves at nodes with the aid of the prediction algorithm. Next, we choose the best cluster starting from primary outputs moving backward in the circuit based on these curves. Experimental results show 16% and 20% reduction in dynamic and leakage power dissipation with 18% area reduction compared to the results of clustering without the replication prediction.

/pdf/low-power-clustering-with-minimum-logic-replication-for-1wq92mtnyg.pdf

Low-power clustering with minimum logic replication for coarse-grained, antifuse based FPGAs

Recent work has presented hierarchical deployment of energy storage devices (ESDs) at the data center, rack, and server levels within a data center, along with a corresponding control framework for peak power shaving and energy cost reduction under (time-of-use) dynamic energy pricing policies However, the prior work does not use a realistic power delivery architecture of the data center with hierarchical ESD structure, and fails to account for some key characteristics such as rate capacity effect of batteries and power losses in various AC/DC and DC/DC converters in the power delivery architecture This paper aims to overcome these shortcomings by (i) adopting a realistic power delivery architecture (from Intel) for centralized ESD structure as the starting point, (ii) presenting a novel power delivery architecture for data centers with hierarchical ESD structure, borrowing the best features of the centralized ESD structure from Intel and the distributed single-level ESD structures from Google and Microsoft, (iii) providing a mathematical framework for the optimal design (ie, ESD provisioning) and control (ie, Scheduling the charging and discharging of various ESDs) of the hierarchical ESD structure to minimize overall energy cost under dynamic energy pricing functions This framework accounts for constraints on ESD volume (for each level) and the overall (annually amortized) capital cost, and power losses due to the rate capacity effect and conversion circuitry The ESD design problem is solved by using a search-based algorithm, whereas the ESD control problem is formulated and solved as a hierarchical convex optimization algorithm Experiments have been conducted using real Google cluster workload based on realistic data center specifications, demonstrating the effectiveness of the proposed optimal design and control framework

Hierarchical Deployment and Control of Energy Storage Devices in Data Centers

The overheads of classical decoding for quantum error correction grow rapidly with the number of logical qubits and their correction code distance. Decoding at room temperature is bottle-necked by refrigerator I/O bandwidth while cryogenic on-chip decoding is limited by area/power/thermal budget. To overcome these overheads, we are motivated by the obser-vation that in the common case (over 90% of the time), error signatures are fairly trivial with high redundancy / sparsity, since the error correction codes are over-provisioned to be able to correct for uncommon worst-case complex scenarios (to ensure substantially low logical error rates). If suitably ex-ploited, these trivial signatures can be decoded and corrected with insigniﬁcant overhead, thereby alleviating the bottlenecks described above, while still handling the worst-case complex signatures by state-of-the-art means. Our proposal, targeting Surface Codes, consists of: our a of

Have your QEC and Bandwidth too!: A lightweight cryogenic decoder for common / trivial errors, and efficient bandwidth + execution management otherwise

This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level. We utilize advances in deep learning to guide an approximate logic synthesis engine to minimize the dynamic power consumption of a given digital CMOS circuit, subject to a predetermined error rate at the primary outputs. Our framework, Deep-PowerX1, focuses on replacing or removing gates on a technology-mapped network and uses a Deep Neural Network (DNN) to predict error rates at primary outputs of the circuit when a specific part of the netlist is approximated. The primary goal of Deep-PowerX is to reduce the dynamic power whereas area reduction serves as a secondary objective. Using the said DNN, Deep-PowerX is able to reduce the exponential time complexity of standard approximate logic synthesis to linear time. Experiments are done on numerous open source benchmark circuits. Results show significant reduction in power and area by up to 1.47× and 1.43× compared to exact solutions and by up to 22% and 27% compared to state-of-the-art approximate logic synthesis tools while having orders of magnitudes lower run-time.

Deep-PowerX: a deep learning-based framework for low-power approximate logic synthesis

Thermoelectric coolers are compact devices that can target hot spots on a VLSI die. These devices are connected electrically in series and controlled together, i.e., all are ON or OFF at the same time. However, spatial and temporal distributions of hot spots on a VLSI die are non-uniform, and therefore, activating all of TECs to address one or a few localized hot spots is not economical. This traditional technique indeed leads to a significant power waste. This paper suggests that adjacent hot spots with the same thermal behavior can be grouped and controlled by a cluster of TECs. A bypass switch for each TEC cluster is added in order to allow selectively turning OFF some TEC clusters which are needed. More precisely, a clustering problem is formulated which aims to minimize the power waste due to excessive use of TECs. Due to the large number of variables in problems of interesting sizes, a greedy heuristic method for solving the problem is introduced. It is shown that the proposed heuristic can reduce the wasted power on average by 81% and also decrease the total TEC power consumption on average by 42%.

Massoud Pedram

Papers

Low-power clustering with minimum logic replication for coarse-grained, antifuse based FPGAs

Hierarchical Deployment and Control of Energy Storage Devices in Data Centers

Have your QEC and Bandwidth too!: A lightweight cryogenic decoder for common / trivial errors, and efficient bandwidth + execution management otherwise

Deep-PowerX: a deep learning-based framework for low-power approximate logic synthesis

Power-efficient control of thermoelectric coolers considering distributed hot spots