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.

Chromatic encoding: a low power encoding technique for digital visual interface

Abstract: This paper presents a low-power encoding technique, called chromatic encoding, for the digital visual interface standard (DVI), a digital serial video interface. Chromatic encoding reduces power consumption by minimizing the transition counts on the DVI. This technique relies on the notion of tonal locality, i.e., the observation - first made in this paper - that the signal differences between adjacent pixels in images follow a Gaussian distribution. Based on this observation, an optimal code assignment is performed to minimize the transition counts. Furthermore, the three-color channels of the DVI may be reciprocally encoded to achieve even more power saving. The idea is that given the signal values from the three-color channels, one or two of these channels are encoded by reciprocal differences with a number of redundant bits used to indicate the selection. The channel selection problem is formulated as a minimum spanning tree problem and solved accordingly. The proposed technique requires only three redundant bits for each 24-bit pixel. Experimental results show up to a 75% power reduction in the DVI.

Estimation of peak power dissipation in VLSI circuits using the limiting distributions of extreme order statistics

Abstract: In this paper, we present a statistical method for estimating the peak power dissipation in very large scale integrated (VLSI) circuits. The method is based on the theory of extreme order statistics and its application to the probabilistic distributions of the cycle-by-cycle power consumption, the maximum-likelihood estimation, and the Monte-Carlo simulation. It enables us to predict the maximum power of a VLSI circuit in the set of constrained input vector pairs as well as the complete set of all possible input vector pairs. The simulation-based nature of the proposed method allows us to avoid the limitations of a gate-level delay model and a gate-level circuit structure. Most significantly, the proposed method produces maximum power estimates to satisfy user-specified error and confidence levels. Experimental results show that this method typically produces maximum power estimates within 5% of the actual value and with a 90% confidence level by only simulating less than 2500 input vectors.

A current source model for CMOS logic cells considering multiple input switching and stack effect

Abstract: This paper presents a current source model (CSM) of a CMOS logic cell, which captures simultaneous switching of multiple inputs while accounting for the effect of internal node voltages of the logic cell. Characterization procedures for various components of the proposed CSM are described and application of the model to output waveform computation is discussed. Experimental results to assess the accuracy and efficiency of the proposed multiple input switching CSM in the context of noise and timing analyses in VLSI circuits are reported.

Charge Recycling in Power-Gated CMOS Circuits

Abstract: The design of a suitable power gating (e.g., multithreshold or super cutoff CMOS) structure is an important and challenging task in sub-90-nm very large scale integration (VLSI) circuits where leakage currents are significant. In designs where the mode transitions are frequent, a significant amount of energy is consumed to turn on or off the power gating structure. It is thus desirable to develop a power gating solution that minimizes the energy consumed during mode transitions. This paper presents such a solution by recycling charge between the virtual power and ground rails immediately after entering the sleep mode and just before wakeup. The proposed method can save up to 43% of the dynamic energy wasted during mode transition while maintaining the wakeup time of the original circuit. It also reduces the peak negative voltage value and the settling time of the ground bounce.

Hierarchical power management with application to scheduling

Abstract: This paper presented a hierarchical power management architecture which aims to facilitate power-awareness in an energy-managed computer (EMC) system with multiple components. The proposed architecture divides PM function into two layers: system-level and component-level. The system-level hierarchical PM was formulated as a concurrent service request flow regulation and application scheduling problem. Experimental results showed that a 25% reduction in the total system energy can be achieved compared to the optimal component-level DPM policy.

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.