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.

Floorplanning with pin assignment

Abstract: A hierarchical technique is presented for floorplanning and pin assignment of general cell layouts. Given a set of cells with their shape lists, a layout aspect ratio, relative positions of the external I/O pads and upper bound delay constraints for a set of critical nets, the authors determine shapes and positions of the cells, locations of the floating pins on cells and a global routing solution such that a linear combination of the layout area, the total interconnection length and constraint violations for critical nets is minimized. Floorplanning, pin assignment and global routing influence one another during the hierarchical steps of the algorithm. The pin assignment algorithm is flexible and allows various user specified constraints such as pre-specified pin locations, feedthrough pins, length-critical nets and planar net topologies. Placement, timing and floorplanning results for a Xerox general cell benchmark are reported. >

Runtime mechanisms for leakage current reduction in CMOS VLSI circuits1,2

Abstract: . This paper describes two runtime mechanisms for reducing the leakage current of a CMOS circuit. In both cases, it is assumed that the system or environment produces a "sleep" signal that can be used to indicate that the circuit is in a standby mode. In the first method, the "sleep" signal is used to shift in a new set of external inputs and pre-selected internal signals into the circuit with the goal of setting the logic values of all of the internal signals so as to minimize the total leakage current in the circuit. This minimization is possible because the leakage current of a CMOS gate is a strong function of the input combination applied to its inputs. In the second method, NMOS and PMOS transistors are added to some of the gates in the circuit to increase the controllability of the internal signals of the circuit and decrease the leakage current of the gates using the "stack effect". This is, however, done carefully so that the minimum leakage is achieved subject to a delay constraint for all input-output paths in the circuit. In both cases, Boolean satisfiability is used to formulate the problems, which are subsequently solved by employing a highly efficient SAT solver. Experimental results on the circuits in the MCNC91 benchmark suite demonstrate that it is possible to reduce the leakage current by up to 70% in VLSI circuits at the expense of a very small overhead.

Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification

Abstract: To cope with the variations and uncertainties that emanate from hardware and application characteristics, dynamic power management (DPM) frameworks must be able to learn about the system inputs and environment and adjust the power management policy on the fly. In this paper we present an online adaptive DPM technique based on model-free reinforcement learning (RL), which is commonly used to control stochastic dynamical systems. In particular, we employ temporal difference learning for semi-Markov decision process (SMDP) for the model-free RL. In addition a novel workload predictor based on an online Bayes classifier is presented to provide effective estimates of the workload states for the RL algorithm. In this DPM framework, power and latency tradeoffs can be precisely controlled based on a user-defined parameter. Experiments show that amount of average power saving (without any increase in the latency) is up to 16.7% compared to a reference expert-based approach. Alternatively, the per-request latency reduction without any power consumption increase is up to 28.6% compared to the expert-based approach.

Therminator: a thermal simulator for smartphones producing accurate chip and skin temperature maps

Abstract: Maintaining safe chip and device skin temperatures in small form-factor mobile devices (such as smartphones and tablets) while continuing to add new functionalities and provide higher performance has emerged as a key challenge. This paper presents Therminator, an early stage, fast, full-device thermal analyzer, which generates accurate steady-state temperature maps of the entire smartphone starting from the Application Processor and other key device components, extending to the skin of the device itself. The thermal analysis is sensitive to detailed device specifications (including its material composition and 3-D layout) as well as different use cases (each case specifying the set of active device components and their activity levels). Therminator considers all major components within the device, builds a corresponding compact thermal model for each component and the whole device, and produces their steady-state temperature maps. Temperature results obtained by using Therminator have been validated against a commercial computational fluid dynamics-based tool, i.e., Autodesk Simulation CFD, and thermocouple measurements on a Qualcomm Mobile Developer Platform. A case study on a Samsung Galaxy S4 using Therminator is provided to relate the device performance to the skin temperature and investigate the thermal path design.

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.