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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
23 Nov 2015
TL;DR: This work aims at finding a design-centered FinFET model with small geometric for circuit and system level simulations and performance prediction of next-generation systems on chip.
Abstract: This work aims at finding a design-centered FinFET model with small geometric for circuit and system level simulations and performance prediction of next-generation systems on chip. A number of devices including the ITRS 7nm multi-gate device are used as examples. While adjusting design parameters for the transistors, a design centering step is included in which the gate workfunction is carefully adjusted to account for the increased power dissipation due to gate length variations. Using a cross-layer framework, compact device models and standard cell libraries are built up for circuit-level and system-level simulations. Simulation results of SRAM cells as well as some combinational/sequential benchmark circuits are shown to compare the device performance in different technologies.

1 citations

Journal ArticleDOI
TL;DR: A dynamic voltage and frequency scaling technique is used to adjust the decoding aptitude of the client while meeting a constraint on the minimum achieved video quality and the notion of a normalized decoding load is introduced.
Abstract: In this paper, we propose an energy-aware MPEG-4 FGS video streaming system with client feedback. In this client-server system, the battery-powered mobile client sends its maximum decoding capability (i.e., its decoding aptitude) to the server in order to help the server determine the additional amount of data (in the form of enhancement layers on top of the base layer) per frame that it sends to the client, and thereby, set its data rate. On the client side, a dynamic voltage and frequency scaling technique is used to adjust the decoding aptitude of the client while meeting a constraint on the minimum achieved video quality. As a measure of energy efficiency of the video streamer, the notion of a normalized decoding load is introduced. It is shown that a video streaming system that maintains this normalized load at unity produces the optimum video quality with no energy waste. We implemented an MPEG-4 FGS video streaming system on an XScale-based testbed in which a server and a mobile client are wirelessly connected by a feedback channel. Based on the actual current measurements in this testbed, we obtain an average of 20% communication energy reduction in the client by making the MPEG-4 FGS streamer energy-aware.

1 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented an analytical model for calculating the read margin of static random access memory (SRAM) cells as a function of different transistors parameters, assuming normal distribution for the threshold voltages of transistors in the presence of process variations.
Abstract: In this work, we present an analytical model for calculating the read margin of static random access memory (SRAM) cells as a function of different transistors parameters. Using this model and assuming normal distribution for the threshold voltages of transistors in the presence of process variations, the probability distribution function (PDF) of the read margin is analytically derived. In addition, the time variation of the PDF due to the negative bias temperature instability (NBTI) effect is also considered in the model. The accuracy of the model is verified by comparing its results with those of HSPICE simulations in 45 and 32 nm technologies. The comparison demonstrates a very high level of accuracy for the proposed model.

1 citations

Journal ArticleDOI
TL;DR: This paper presents a synthesis methodology for ECL circuits based on a mixed voltage-current signal representation and operation defined on the voltage and current signals and concludes by presenting an algebraic system which is suitable for current signal Representation and operation on currents.
Abstract: This paper presents a synthesis methodology for ECL circuits based on a mixed voltage-current signal representation and operation defined on the voltage and current signals. The ideas presented in this paper are then demonstrated on the design of an ECL 1-bit full adder. The paper concludes by presenting an algebraic system which is suitable for current signal representation and operation on currents.

1 citations

Proceedings ArticleDOI
26 Mar 2013
TL;DR: The results of this study show that, in some cases, the lifetime of the extensible processors is decreased, but in most cases the extended ISA is able to improve the lifetime compared to the baseline processor.
Abstract: This paper studies the impact of the delay degradation arising from the Negative Bias Temperature Instability (NTBI) effect on the extended instruction set architecture (ISA) and the ALU design of extensible processors. In particular, the NBTI delay degradation on the performance of extensible processors is modeled during the conventional design flow. While the results of this study show that, in some cases, the lifetime of the extensible processors is decreased, in most cases, the extended ISA is able to improve the lifetime compared to the baseline processor. Next, three different design flows are presented to lower the NBTI effect. These flows are based on reducing the stress on custom instructions (CIs) by considering the number of occurrences of the selected CIs in the data flow graph of the target application and by pruning the CIs whose NBTI-induced delay degradations are large and result in slow down of the extensible processor. Experimental results assess the effectiveness of the proposed methods.

1 citations


Cited by
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Journal ArticleDOI

[...]

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