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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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Journal ArticleDOI
TL;DR: A design methodology for controlling the switching times of the output drivers to minimize the ground bounce and a closed form expression for the peak value of the differential-mode component of the ground bounces in terms of the on-chip decoupling capacitor are provided.
Abstract: This paper is concerned with the analysis and optimization of the ground bounce in digital CMOS circuits. First, an analytical method for calculating the ground bounce is presented. The proposed method relies on accurate models of the short-channel MOS device and the chip-package interface parasitics. Next the effect of ground bounce on the propagation delay and the optimum tapering factor of a multistage buffer is discussed and a mathematical relationship for total propagation delay in the presence of the ground bounce is obtained. Effect of an on-chip decoupling capacitor on the ground bounce waveform and circuit speed is analyzed next and a closed form expression for the peak value of the differential-mode component of the ground bounce in terms of the on-chip decoupling capacitor is provided. Finally, a design methodology for controlling the switching times of the output drivers to minimize the ground bounce is presented.

122 citations

Journal ArticleDOI
TL;DR: The DVFS transition overhead is redefined including the underclocking-related losses in a DVFS-enabled microprocessor, additional inductor IR losses, and power losses due to discontinuous-mode DC-DC conversion.
Abstract: Dynamic voltage and frequency scaling (DVFS) has been studied for well over a decade. Nevertheless, existing DVFS transition overhead models suffer from significant inaccuracies; for example, by incorrectly accounting for the effect of DC–DC converters, frequency synthesizers, voltage, and frequency change policies on energy losses incurred during mode transitions. Incorrect and/or inaccurate DVFS transition overhead models prevent one from determining the precise break-even time and thus forfeit some of the energy saving that is ideally achievable. This paper introduces accurate DVFS transition overhead models for both energy consumption and delay. In particular, we redefine the DVFS transition overhead including the underclocking-related losses in a DVFS-enabled microprocessor, additional inductor IR losses, and power losses due to discontinuous-mode DC–DC conversion. We report the transition overheads for a desktop, a mobile and a low-power representative processor. We also present DVFS transition overhead macromodel for use by high-level DVFS schedulers.

122 citations

Proceedings ArticleDOI
29 May 2013
TL;DR: Optimization of the interaction distance between qubits to map a quantum circuit into one-dimensional quantum architectures is addressed and a lookahead technique is applied to improve the cost of the proposed solution.
Abstract: Optimization of the interaction distance between qubits to map a quantum circuit into one-dimensional quantum architectures is addressed. The problem is formulated as the Minimum Linear Arrangement (MinLA) problem. To achieve this, an interaction graph is constructed for a given circuit, and multiple instances of the MinLA problem for selected subcircuits of the initial circuit are formulated and solved. In addition, a lookahead technique is applied to improve the cost of the proposed solution which examines different subcircuit candidates. Experiments on quantum circuits for quantum Fourier transform and reversible benchmarks show the effectiveness of the approach.

121 citations

Proceedings ArticleDOI
18 Aug 2010
TL;DR: This work introduces a HEES (hybrid EES) system comprising heterogeneous EES elements and builds on the concepts of computer memory system architecture and management in order to achieve the attributes of an ideal EES system through appropriate allocation and organization of various types of Ees elements.
Abstract: Electrical energy is a high quality form of energy that can be easily converted to other forms of energy with high efficiency and, even more importantly, it can be used to control lower grades of energy quality with ease. However, building a cost-effective electrical energy storage (EES) system is a challenging task despite steady advances in the design and manufacturing of EES elements including various battery and supercapacitor technologies. As of today, no single type of EES element fulfills high energy density, high power delivery capacity, low cost per unit of storage, long cycle life, low leakage, and so on at the same time. Unlike conventional EES systems, we introduce a HEES (hybrid EES) system comprising heterogeneous EES elements. Our proposed HEES system builds on the concepts of computer memory system architecture and management in order to achieve the attributes of an ideal EES system through appropriate allocation and organization of various types of EES elements. We also introduce a HEES design considerations which should be taken into account to optimize the amortized cost for the system, including the initial cost (cost per capacity), the operating cost (efficiency), the maintenance cost (cycle life and disposal cost), and so forth.

119 citations

Proceedings Article
01 Jan 1998
TL;DR: Simulation using SPICE and a 1 /spl mu/ technology shows that this proposed double-edge-triggered (DET) flip-flop has ideal logic functionality, a simpler structure, lower delay time, and higher maximum data rate compared to other existing CMOS DET flip- flops.
Abstract: The logic construction of a double-edge-triggered (DET) flip-flop, which can receive input signal at two levels of the clock, is analyzed and a new circuit design of CMOS DET flip-flop is proposed Simulation using SPICE and a 1 micron technology shows that this DET flip-flop has ideal logic functionality, a simpler structure, lower delay time and higher maximum data rate compared to other existing CMOS DET flipflops By simulating and comparing the proposed DET flip-flop with the traditional single-edge-triggered (SET) flip-flop, it is shown that the proposed DET flip-flop reduces power dissipation by half while keeping the same date rate

117 citations


Cited by
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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