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.

A Theoretical Framework for Quality Estimation and Optimization of DSP Applications Using Low-Power Approximate Adders

Abstract: In this paper, we present a framework for analytically estimating the output quality of common digital signal processing (DSP) blocks that utilize approximate adders. The framework is based on considering the error of approximate adders as an additive noise (approximation noise) that disturbs the output of the DSP block in question. A signal processing theoretical modeling approach for describing the power of the approximation noise which is the integral of error spectral density over the bandwidth, is developed. The output qualities of DSP blocks, such as finite impulse response filter, discrete cosine transform, and fast Fourier transform, which utilize approximate adders, are thus estimated. The accuracy of the proposed framework is evaluated by comparing mathematical model predictions to simulation results by using the signal-to-noise ratio (SNR) metric. The inaccuracy of the SNRs predicted by the framework was, on average, less than 2.5dB compared with that obtained from simulations. Therefore, a mathematical optimization approach based on Lagrange Multipliers for optimizing design parameters is also presented. The optimization is realized by choosing a proper configuration of the target block, such as determining the data width of the inexact computation part for each approximate adder in the design.

Charge replacement in hybrid electrical energy storage systems

Abstract: Hybrid electrical energy storage (HEES) systems are composed of multiple banks of heterogeneous electrical energy storage (EES) elements with distinctive properties. Charge replacement in a HEES system (i.e., dynamic assignment of load demands to EES banks) is one of the key operations in the system. This paper formally describes the global charge replacement (GCR) optimization problem and provides an algorithm to find the near-optimal GCR control policy. The optimization problem is formulated as a mixed-integer nonlinear programming problem, where the objective function is the charge replacement efficiency. The constraints account for the energy conservation law, efficiency of the charger/converter, the rate capacity effect, and self-discharge rates plus internal resistances of the EES element arrays. The near-optimal solution to this problem is obtained while considering the state of charges (SoCs) of the EES element arrays, characteristics of the load devices, and estimates of energy contributions by the EES element arrays. Experimental results demonstrate significant improvements in the charge replacement efficiency in an example HEES system comprised of banks of battery and supercapacitor elements with a high-power pulsed military radio transceiver as the load device.

Charge allocation for hybrid electrical energy storage systems

Abstract: Hybrid electrical energy storage (HEES) systems, composed of multiple banks of heterogeneous electrical energy storage (EES) elements with their unique strengths and weaknesses, have been introduced to efficiently store and retrieve electrical energy while attaining performance metrics that are close to their respective best values across their constituent EES elements. This paper is the first paper to formally describe the charge allocation problem and provide a systematic solution method aiming at the maximum charge allocation efficiency, which performing proper distribution of the incoming power to selected destination banks. We introduce a generalized HEES architecture and build the corresponding electrical circuit models of the chargers and banks. We formulate a mixed integer nonlinear optimization problem, where the objective function is the global charge allocation efficiency, and the constraints are energy conservations, with careful consideration of the conversion power loss in the chargers, rate capacity effect and self-discharge of the EES elements, charge transfer losses, and so on. We present a rigorous algorithm to achieve a near-optimal global charge allocation efficiency for long-term charge allocation process (i.e., tens of hours.) Experimental results based on a photovoltaic cell array as the incoming power source and a HEES system comprised on batteries and supercapacitors demonstrate a significant gain in charge allocation efficiency for the proposed algorithm.

An electricity trade model for microgrid communities in smart grid

Abstract: Distributed microgrid network is the major trend of future smart grid, which contains various kinds of renewable power generation centers and a small group of energy users. In the distributed power system, each microgrid acts as a “prosumer” (producer and consumer) and maximizes its own social welfare. In addition, different microgrids can interact among each other through trading over a marketplace. In this paper, two models are introduced for microgrids to deal with the welfare maximization problems. In the first model, a microgrid is considered as a closed economy group and decides the optimal power generation distribution in terms of time. In the second model, each microgrid can trade with its neighborhoods and thus achieve a welfare increase from making use of its comparative advantage on power generation during a certain period of time. For each model, an efficient solution is presented. Experimental result shows the accuracy and efficiency of our presented solutions.

Analysis and reduction of capacitive coupling noise in high-speed VLSI circuits

Abstract: Scaling the minimum feature size of VLSI circuits to sub-quarter micron and its clock frequency to 2 GHz has caused crosstalk noise to become a serious problem, that degrades the performance and reliability of high speed integrated circuits. This paper presents an efficient method for computing the capacitive crosstalk in sub-quarter micron VLSI circuits. In particular, we provide closed-form expressions for the peak amplitude, the pulse width, and the time-domain waveform of the crosstalk noise. Experiments show that our analytical predictions are at least two times better than the previous models in terms of the prediction accuracy. More precisely, experimental results show that the maximum error of our predictions is less than 10% while the average error is only 4%. Finally, based on the proposed analytical models, we discuss the effects of transistor sizing and buffering on crosstalk noise reduction in VLSI circuits.

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.