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.

TFA: a threshold-based filtering algorithm for propagation delay and slew calculation of high-speed VLSI interconnects

Abstract: This paper describes an efficient threshold-based filtering algorithm (TFA) for calculating the interconnect delay and slew (transition time) in high-speed VLSI circuits. The key idea is to divide the circuit nets into three groups of low, medium and high complexity nets, whereby for low and medium complexity nets either the first moment of the impulse response or the first and second moments are used. For the high-complexity nets, which are encountered infrequently, TFA resorts to the AWE method. The key contribution of the paper is to come up with very effective and efficient way of classifying the nets into these three groups. Experimental results show that on a large industrial circuit using a state-of-the-art commercial timing analysis that incorporates TFA, we were able to achieve delay and slew estimation accuracies that are quite comparable with the full-blown AWE-based calculators at runtimes that were only 14% higher than those of a simple Elmore-delay calculator.

Off-chip Latency-Driven Dynamic Voltage and Scaling for an MPEG Decoding

Abstract: paper describes n dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption using the computational workload decomposition. This technique decomposes the workload for decoding a frame into on- chip and off-chip workloads. The execution time required for the on- chip workload is CPU frequency-dependent, whereas the off-chip workload aecution time does not change, regardless of the CPU frequency, resulting in the maximum energv savings by setting the minimum frequency during off-chip workload execution time, without causing any delay penalty. This workload decomposition is performed using a performance-monitoring unit (PMU) in the XScale-processor, which provides various statistics such as cache hit/miss and CPUstall, due to data dependency at nm time. The on- chip workload for an incoming frame is predicted using a frame- hased history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode theframe. To guarantee a quality of service (QoS) constraint, a prediction error compensation method, called inter-frame compensation, is proposed in which the on-chip- workload prediction error is diffused into subsequenrpames such that mn time frame rates change smoothly. The proposed DVFS algorithm has been implemented on an XScrrle-based Testbed. Detailed current measurements on this platform demonstrate signifirant CPU energv savings ranging from 50% to 80% depending on the video clip.

Distributed load demand scheduling in smart grid to minimize electricity generation cost

Abstract: Load demand scheduling of electricity consumers is an effective way to alleviate the peak power demand on the electricity grid and to combat the mismatch between generation and consumption. In this paper, we consider a scenario where multiple users cooperate to perform load demand scheduling in order to minimize the electricity generation cost. With the help of a central controller in the grid, a globally optimal solution can be achieved. However, this centralized solution may not always be feasible since it requires a huge amount of communication and the grid may not be equipped with such a central controller at all. Therefore, we propose a distributed load demand scheduling algorithm where each end user schedules its own tasks based on the partial information provided by other users. Simulation results show that this distributed load demand scheduling is able to achieve near-optimal solutions that has very little performance degradation compared to the centralized method.

Analysis and optimization of static power considering transition dependency of leakage current in VLSI circuits

Abstract: We show that leakage current in VLSI circuits is not only a function of the current state (input combination) of a combinational circuit but also is dependent on the state history (previous input combinations). As an example application of the transition-dependent leakage model, we extend a known technique for calculating and applying the minimum leakage input vector to a combinational circuit in the standby mode to one which calculates and applies a pair of input vectors to initialize the circuit to the minimum leakage configuration.

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.