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.

Dual-Quality 4:2 Compressors for Utilizing in Dynamic Accuracy Configurable Multipliers

Abstract: In this paper, we propose four 4:2 compressors, which have the flexibility of switching between the exact and approximate operating modes. In the approximate mode, these dual-quality compressors provide higher speeds and lower power consumptions at the cost of lower accuracy. Each of these compressors has its own level of accuracy in the approximate mode as well as different delays and power dissipations in the approximate and exact modes. Using these compressors in the structures of parallel multipliers provides configurable multipliers whose accuracies (as well as their powers and speeds) may change dynamically during the runtime. The efficiencies of these compressors in a 32-bit Dadda multiplier are evaluated in a 45-nm standard CMOS technology by comparing their parameters with those of the state-of-the-art approximate multipliers. The results of comparison indicate, on average, 46% and 68% lower delay and power consumption in the approximate mode. Also, the effectiveness of these compressors is assessed in some image processing applications.

Design considerations for battery-powered electronics

Abstract: In this paper, we consider the problem of maximizing the battery life (or duration of service) in battery-powered CMOS circuits. We first show that the battery efficiency (or utilization factor) decreases as the average discharge current from the battery increases. The implication is that the battery life is a super-linear function of the average discharge current. Next we show that even when the average discharge current remains the same, different discharge current profiles (distributions) may result in very different battery lifetimes. In particular, the maximum battery life is achieved when the variance of the discharge current distribution is minimized. Analytical derivations and experimental results underline the importance of the correct modeling of the battery-hardware system as a whole and provide a more accurate basis (i.e., the battery discharge times delay product) for comparing various low power optimization methodologies and techniques targeted toward battery-powered electronics. Finally, we calculate the optimal value of V/sub dd/ for a battery-powered VLSI circuit so as to minimize the product of the battery discharge times the circuit delay.

Power conscious CAD tools and methodologies: a perspective

Abstract: Power consumption is rapidly becoming an area of growing concern in IC and system design houses. Issues such as battery life, thermal limits, packaging constraints and cooling options are becoming key factors in the success of a product. As a consequence, IC and system designers are beginning to see the impact of power on design area, design speed, design complexity and manufacturing cost. While process and voltage scaling can achieve significant power reductions, these are expensive strategies that require industry momentum, that only pay off in the long run. Technology independent gains for power come from the area of design for low power which has a much higher return on investment (ROI). But low power design is not only a new area but is also a complex endeavour requiring a broad range of synergistic capabilities from architecture/microarchitecture design to package design. It changes traditional IC design from a two-dimensional problem (Area/performance) to a three-dimensional one (Area/Performance/Power). This paper describes the CAD tools and methodologies required to effect efficient design for low power. It is targeted to a wide audience and tries to convey an understanding of the breadth of the problem. It explains the state of the art in CAD tools and methodologies. The paper is written in the form of a tutorial, making it easy to read by keeping the technical depth to a minimum while supplying a wealth of technical references. Simultaneously the paper identifies unresolved problems in an attempt to incite research in these areas. Finally an attempt is made to provide commercial CAD tool vendors with an understanding of the needs and time frames for new CAD tools supporting low power design. >

Technology Decomposition and Mapping Targeting Low Power Dissipation

Abstract: In this paper, we address the problem of minimizing the average power dissipation during the technology dependent phase of logic synthesis. Our approach consists of two steps. In the first step, we generate a NAND decomposition of an optimized Boolean network such that the sum of average switching rates for all nodes in the network is minimum. Our power-efficient decomposition procedure is optimal for dynamic CMOS circuits with uncorrelated input signals and produces very good results for static CMOS. In the second step, we perform a power efficient technology mapping that finds an optimal power-delay trade-off value (subject to the unknown load problem) for given timing constraints. We obtain an average of 21% improvement in power at the expense of 12.6% increase in area and without any degradation in performance on a number of benchmarks.

Frame-based dynamic voltage and frequency scaling for a MPEG decoder

Abstract: This paper describes a dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption while maintaining a quality of servic(QoS) constraint. The computational workload for an incoming frame is predicted using a frame-based history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode the frame. More precisely, the required decoding time for each frame is separated into two parts: a frame-dependent (FD) part and a frame-independent (FI) part. The FD part varies greatly according to the type of the incoming frame whereas the FI part remains constant regardless of the frame type. In the DVFS scheme presented in this paper the FI part is used to compensate for the prediction error that may occur during the FD part such that a significant amount of energy can be saved while meeting the frame rate requirement. The proposed DVFS algorithm has been implemented on a StrongArm-1110 based evaluation board. Measurement results demonstrate a higher than 50% CPU energy saving as a result of DVFS.

I and i

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 …

Machine learning

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.).

Pattern Recognition and Machine Learning

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.