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Massoud Pedram

Bio: Massoud Pedram is an academic researcher from University of Southern California. The author has contributed to research in topics: Energy consumption & CMOS. The author has an hindex of 77, co-authored 780 publications receiving 23047 citations. Previous affiliations of Massoud Pedram include University of California, Berkeley & Syracuse University.


Papers
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Proceedings ArticleDOI
07 Nov 1988
TL;DR: A combination of top-down and bottom-up heuristics is used to make best use of a hierarchical description and experimental results show a considerable improvement over previous approaches.
Abstract: Placement and related aspects of the BEAR macrocell layout system are described. A combination of top-down and bottom-up heuristics is used to make best use of a hierarchical description. The interdependency of placement and routing is considered explicitly. Experimental results show a considerable improvement over previous approaches. >

27 citations

Proceedings ArticleDOI
27 Jun 2014
TL;DR: To address the self-similarity and non-stationarity characteristics of the workload profile in a cloud computing system, fractal modeling techniques similar to some cyber-physical system (CPS) applications are exploited.
Abstract: The problem of big data analytics is gaining increasing research interest because of the rapid growth in the volume of data to be analyzed in various areas of science and technology. In this paper, we investigate the characteristics of the cloud computing requests received by the cloud infrastructure operators. The cluster usage dataset released by Google is thoroughly studied. To address the self-similarity and non-stationarity characteristics of the workload profile in a cloud computing system, fractal modeling techniques similar to some cyber-physical system (CPS) applications are exploited. A trace-based prediction of the job inter-arrival time and aggregated resource request sent to server cluster in the near future is effectively performed by solving fractional-order differential equations. The distributions of important parameters including job/task duration time and resource request per task in terms of CPU, memory, and storage are extracted from the cluster dataset are fitted using the alpha-stable distribution.

27 citations

Journal ArticleDOI
TL;DR: Various approaches for power analysis and minimization at the logic level including, amongst others, pattern-independent probabilistic and symbolic simulation techniques for power estimation and low-power techniques for state assignment, logic restructuring, logic decomposition, technology mapping and pin ordering are described.
Abstract: This paper describes various approaches for power analysis and minimization at the logic level including, amongst others, pattern-independent probabilistic and symbolic simulation techniques for power estimation and low-power techniques for state assignment, logic restructuring, logic decomposition, technology mapping and pin ordering.

27 citations

Proceedings ArticleDOI
24 Mar 2014
TL;DR: A novel SoH degradation model of battery for charging/discharging cycles with arbitrary patterns is derived and a near-optimal charge management policy focusing on extending the cycle life of battery elements in the HEES systems while simultaneously improving the overall cycle efficiency is presented.
Abstract: Hybrid electrical energy storage (HEES) systems consisting of heterogeneous electrical energy storage (EES) elements are proposed to exploit the strengths of different EES elements and hide their weaknesses. The cycle life of the EES elements is one of the most important metrics. The cycle life is directly related to the state-of-health (SoH), which is defined as the ratio of full charge capacity of an aged EES element to its designed (or nominal) capacity. The SoH degradation models of battery in the previous literature can only be applied to charging/discharging cycles with the same state-of-charge (SoC) swing. To address this shortcoming, this paper derives a novel SoH degradation model of battery for charging/discharging cycles with arbitrary patterns. Based on the proposed model, this paper presents a near-optimal charge management policy focusing on extending the cycle life of battery elements in the HEES systems while simultaneously improving the overall cycle efficiency.

27 citations

Proceedings ArticleDOI
01 Dec 1995
TL;DR: The feasibility of generating the set of all PPIs and the increased complexity of solving the minimum covering problem are analyzed by deriving an upper bound on the expected number of PPIs which shows it to be linearly proportional to the number of prime implicants of the function.
Abstract: We study the problem of two-level logic minimization for low power in static CMOS circuits. We start by defining Power Prime Implicants (PPIs) which identify the set of all implicants that are sufficient and necessary for obtaining a minimum power solution. We then provide an efficient algorithm for generating the set of all PPIs of a function. The set of all PPIs is then used in a minimum covering problem to find the best power solution. The feasibility of generating the set of all PPIs and the increased complexity of solving the minimum covering problem are analyzed by deriving an upper bound on the expected number of PPIs which shows it to be linearly proportional to the number of prime implicants of the function. The results of our experiments are then used to draw conclusions on the effectiveness of low power two-level logic minimization.

27 citations


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Journal ArticleDOI

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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: 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.

10,141 citations