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 framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting

Abstract: Energy harvesting is a promising technique to overcome the limitation imposed by the finite energy capacity of batteries in conventional battery-powered embedded systems. In particular, the question of how one can achieve full energy autonomy (i.e., perpetual, battery-free operation) of a real-time embedded system with an energy harvesting capability (RTES-EH) by applying a global control strategy is investigated. The energy harvesting module is comprised of a Photovoltaic (PV) panel for harvesting energy and a supercapacitor for storing any excess energy. The global controller performs optimal operating point tracking for the PV panel, state-of-charge management for the supercapacitor, and energy-harvesting-aware real-time task scheduling with dynamic voltage and frequency scaling (DVFS) in the embedded load device. The controller, which accounts for dynamic V-I characteristics of the PV panel, terminal voltage variation and self-leakage of the supercapacitor, and power losses in voltage converters, employs a cascaded feedback control structure with an inner control loop determining the V-I operating point of the PV panel and an outer supervisory control loop performing real-time task scheduling and setting the voltage and frequency level in the embedded load device (to keep the state-of-charge of the supercapacitor in a desirable range). Experimental results show that the proposed global controller lowers the task drop rate in a RTES-EH by up to 60% compared with baseline controller within the same service time.

Alleviating routing congestion by combining logic resynthesis and linear placement

Abstract: In this approach, the logic is restructured using an intermediate placement solution and then the placement is adjusted to match the new logic structure. This ability to change logic structure during layout allows one to obtain channel density reductions that are not possible by physical design operations such as lateral shifting, pin permutation, and channel routing. Parts on an industrial chip have been resynthesized using a prototype program implementing these ideas with an average of 11.2% reduction in bit slice area compared to the original designs. >

A study of the effectiveness of CPU consolidation in a virtualized multi-core server system

Abstract: The focus of this paper is on dynamic power management in virtualized multi-core server systems. The paper starts by analyzing the effect of virtualization and CPU consolidation on power dissipation and performance (latency) of such systems, and concludes by presenting two new CPU consolidation algorithms for multi-core servers. The paper also reports an extensive set of experimental results founded on a realistic multi-core server system setup and well-developed benchmarks, i.e., SPEC2K and SPECWeb2009 and obtained through hardware measurements.

Timing-driven placement for general cell layout

Abstract: A hierarchical technique is presented for the timing-driven placement of the general cells. It is assumed that maximum interconnection delays for nets are given. These timing constraints are transformed to net length constraints using technology-and process-dependent parameters, circuit-specific data such as input capacitance and output drivability, and the structural description, of the circuit. The problem is divided into a bottom-up clustering phase and a top-down enumerative placement phase. Both the natural connectivities and the net length constraints are considered during the bottom-up phase in order to generate a hierarchical cluster tree. During the top-down placement phase, they place each node of the cluster tree so that the layout area and total interconnection length are minimized while satisfying the net length constraints. >

Automatic Layout of Silicon-On-Silicon Hybrid Packages

Abstract: In modern, high speed computer systems, performance and density limits are being set more by interconnection and packaging constraints than by transistor characteristics. The most severe limitation comes from the single-chip packages that carry the VLSI circuits. Multichip, silicon-on-silicon hybrid packages can significantly improve performance by eliminating this level of packaging. A system has been developed to automatically generate hybrid layouts given a schematic description and layouts of the VLSI circuits. This paper describes the hybrid technology, the design automation system foundation, and the hybrid layout system. This layout method, in combination with the fabrication technology, produces layouts that are 5 to 8 times more dense than the same circuits implemented with single-chip packages on printed circuit boards. Simulations show that clock speeds can be increased by a factor of two.

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.