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.

Online estimation of the remaining energy capacity in mobile systems considering system-wide power consumption and battery characteristics

Abstract: Emerging mobile systems integrate a lot of functionality into a small form factor with a small energy source in the form of rechargeable battery. This situation necessitates accurate estimation of the remaining energy in the battery such that user applications can be judicious on how they consume this scarce and precious resource. This paper thus focuses on estimating the remaining battery energy in Android OS-based mobile systems. This paper proposes to instrument the Android kernel in order to collect and report accurate subsystem activity values based on real-time profiling of the running applications. The activity information along with offline-constructed, regression-based power macro models for major subsystems in the smartphone yield the power dissipation estimate for the whole system. Next, while accounting for the rate-capacity effect in batteries, the total power dissipation data is translated into the battery's energy depletion rate, and subsequently, used to compute the battery's remaining lifetime based on its current state of charge information. Finally, this paper describes a novel application design framework, which considers the batterys state-of-charge (SOC), batterys energy depletion rate, and service quality of the target application. The benefits of the design framework are illustrated by examining an archetypical case, involving the design space exploration and optimization of a GPS-based application in an Android OS.

An approach for multilevel logic optimization targeting low power

Abstract: This paper shows that using don't cares computed for area optimization during local node minimization may result in an increase in the power consumption of other nodes in a Boolean network. It then presents techniques for computing a subset of observability and satisfiability don't care conditions that can be used freely to optimize the local function of nodes. The concept of minimal variable support is then used to optimize the local function of each node for minimum power using its power relevant don't care set, that is, to reimplement the local function using a modified support that has a lower switching activity. Empirical results on a set of benchmark circuits are presented and discussed.

Architectural power optimization by bus splitting

Abstract: A split-bus architecture is proposed to improve the power dissipation for global data exchange among a set of modules. The resulting bus splitting problem is formulated and solved combinatorially. Experimental results show that the power saving of the split-bus architecture compared to the monolithic-bus architecture varies from 16% to 50%, depending on the characteristics of the data transfer among the modules and the configuration of the split bus. The proposed split-bus architecture can be extended to multi-way split-bus when a large number of modules are to be connected.

Exact and Approximate Methods of Switching Activity Estimation in Sequential Logic Circuits

Abstract: Recently developed methods for power estimation have primarily focused on combinational logic. In this paper, we present a framework for the e cient and accurate estimation of average power dissipation in sequential circuits. Switching activity is the primary cause of power dissipation in CMOS circuits. Accurate, average switching activity estimation for sequential circuits is considerably more di cult than for combinational circuits, because the probability of the circuit being in each of its possible states has to be calculated. The Chapman-Kolmogorov equations can be used to accurately estimate the power dissipation of sequential circuits by computing the exact state probabilities in steady state. However, the Chapman-Kolmogorov method requires the solution of a linear system of equations of size 2 N , where N is the number of ipops in the machine. We describe a comprehensive framework for exact and approximate switching activity estimation in this paper. The basic computation step is the solution of a non-linear system of equations. Increasing the number of variables or the number of equations in the system results in increased accuracy. For a wide variety of examples, we show that the approximation scheme is within 1 3% of the exact method, but is orders of magnitude faster for large circuits. Previous sequential switching activity estimation methods can have signi cantly greater inaccuracies. J. Monteiro and S. Devadas were supported in part by the Defense Advanced Research Projects Agency under contract N00014-91-J-1698 and in part by a NSF Young Investigator Award with matching funds from Mitsubishi and IBM Corporation. C-Y. Tsui, M. Pedram and A. Despain are with the Department of Electrical Engineering at the University of Southern California. J. Monteiro and S. Devadas are with the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, Cambridge. B. Lin is with IMEC, Belgium. Latches Combinational Logic P r i m a r y I n p u t s P r i m a r y O u t p u t s Present States Next States Clock Figure 1: A Synchronous Sequential Circuit

Opportunities for Machine Learning in Electronic Design Automation

Abstract: The rise of machine learning (ML) has introduced many opportunities for computer-aided-design, VLSI design, and their intersection. Related to computer-aided design, we review several classical CAD algorithms which can benefit from ML, outline the key challenges, and discuss promising approaches. In particular, because some of the existing ML accelerators have used asynchronous design, we review the state-of-the-art in asynchronous CAD support, and identify opportunities for ML within these flows.

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.