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.

Stochastic modeling of a power-managed system-construction and optimization

Abstract: The goal of a dynamic power management policy is to reduce the power consumption of an electronic system by putting system components into different states, each representing a certain performance and power consumption level. The policy determines the type and timing of these transitions based on the system history, workload, and performance constraints. In this paper we propose a new abstract model of a power-managed electronic system. We formulate the problem of system-level power management as a controlled optimization problem based on the theories of continuous-time Markov derision processes and stochastic networks. This problem is solved exactly using linear programming or heuristically using "policy iteration." Our method is compared with existing heuristic methods for different workload statistics. Experimental results show that the power management method based on a Markov decision process outperforms heuristic methods by as much as 44% in terms of power dissipation savings for a given level of system performance.

A Near-Optimal Model-Based Control Algorithm for Households Equipped With Residential Photovoltaic Power Generation and Energy Storage Systems

Abstract: Integrating residential photovoltaic (PV) power generation and energy storage systems into the Smart Grid is an effective way of reducing fossil fuel consumptions. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing, since consumers can use their PV-based energy generation and controllable energy storage devices for peak shaving on their power demand profile, thereby minimizing their electricity bill. A realistic electricity pricing function is considered with billing period of a month, comprising both an energy price component and a demand price component. Due to the characteristics of electricity price function and energy storage capacity limitation, the residential storage control algorithm should 1)utilize PV power generation and load power consumption predictions and 2)account for various energy loss components during system operation, including energy loss components due to rate capacity effect in the storage system and power dissipation of the power conversion circuitry. A near-optimal storage control algorithm is proposed accounting for these aspects. The near-optimal algorithm, which controls the charging/discharging of the storage system, is effectively implemented by solving a convex optimization problem at the beginning of each day with polynomial time complexity. For further improvement, the reinforcement learning technique is adopted to adaptively determine the residual energy in the storage system at the end of each day in a billing period.

Supervised Learning Based Power Management for Multicore Processors

Abstract: This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the occupancy state of a global service queue) and then uses this predicted state to look up the optimal power management action (eg, voltage-frequency setting) from a precomputed policy table The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce the overhead of the PM which has to repetitively determine and assign voltage-frequency settings for each processor core in the system Experimental results demonstrate that the proposed supervised learning based power management technique ensures system-wide energy savings under rapidly and widely varying workloads

RoBA Multiplier: A Rounding-Based Approximate Multiplier for High-Speed yet Energy-Efficient Digital Signal Processing

Abstract: In this paper, we propose an approximate multiplier that is high speed yet energy efficient. The approach is to round the operands to the nearest exponent of two. This way the computational intensive part of the multiplication is omitted improving speed and energy consumption at the price of a small error. The proposed approach is applicable to both signed and unsigned multiplications. We propose three hardware implementations of the approximate multiplier that includes one for the unsigned and two for the signed operations. The efficiency of the proposed multiplier is evaluated by comparing its performance with those of some approximate and accurate multipliers using different design parameters. In addition, the efficacy of the proposed approximate multiplier is studied in two image processing applications, i.e., image sharpening and smoothing.

Gated clock routing for low-power microprocessor design

Abstract: This paper presents a zero-skew gated clock routing technique for VLSI circuits. Gated clock trees include masking gates at the internal nodes of the clock tree, which are selectively turned on and off by the gate control signals during the active and idle times of the circuit modules to reduce the switched capacitance of the clock tree. We construct a clock-tree topology based on the locations and the activation frequencies of the modules, while the locations of the internal nodes of the clock tree (and, hence, the masking gates) are determined using a dynamic programming approach followed by a gate reduction heuristic. This work assumes that the gates are turned on/off by a centralized controller. Therefore, the additional power and routing area incurred by the controller and the gate control signal routing are examined. Various tradeoffs between power and area for different design options and module activities are discussed and detailed experimental results are presented. Finally, good design practices for implementing the gated clocks are suggested.

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.