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.

Energy Consumption and Lifetime Improvement of Coarse-Grained Reconfigurable Architectures Targeting Low-Power Error-Tolerant Applications

Abstract: In this work, the application of a voltage over-scaling (VOS) technique for improving the lifetime and reliability of coarse-grained reconfigurable architectures (GCRAs) is presented. The proposed technique, which may be applied to CGRAs used as accelerators for low-power, error-tolerant applications, reduces the (strongly voltage-dependent) wearout effects and the energy consumption of processing elements (PEs) whenever the error impact on the output quality degradation can be tolerated. This provides us with the ability to lessen the wearout and reduce energy consumption of PEs when accuracy requirement for the results is rather low. Multiple degrees of computational accuracy can be achieved by using different overscaled voltage levels for the PEs. The efficacy of the proposed technique is studied by considering the bias temperature instability. The study is performed for two error-resilient applications. The CGRAs are implemented with 15nm FinFET operating at a nominal supply voltage of 0.8V. In addition, supply voltages of 0.75, 0.7, 0.65, and 0.6V are considered as overscaled voltage levels for this technology. Based on the quality constraint requirements of the benchmarks, optimum overscaled voltage levels for various PEs are determined and utilized. The approach may provide considerable lifetime and energy consumption improvements over those of the conventional exact and approximate computation approaches.

VITA: variation-aware interconnect timing analysis for symmetric and skewed sources of variation considering variational ramp input

Abstract: As technology scales down, timing verification of digital integrated circuits becomes an extremely difficult task due to statistical variations in the gate and wire delays. Statistical timing analysis techniques are being developed to tackle this important problem. In this paper, we propose a new framework for handling variation-aware interconnect timing analysis in which the sources of variation may have symmetric or skewed distributions. To achieve this goal, we express the resistance and capacitance of a line in canonical first order forms and then use these to compute the circuit moments. The variational moments are subsequently used to compute the interconnect delay and slew at each node of an RC tree. For this step, we combine known closed-form delay metrics such as Elmore and AWE-based algorithms to take advantage of the efficiency of the first category and the accuracy of the second. Experimental results show an average error of 2% for interconnect delay and slew with respect to SPICE-based Monte Carlo simulations.

Negotiation-based resource provisioning and task scheduling algorithm for cloud systems

Abstract: Cloud computing has drawn significant attention from both academia and industry as an emerging computing paradigm where data, applications, or processing power are provided as services through the Internet. Cloud computing extends the existing computing infrastructure owned by the cloud service providers (CSPs) to achieve the economies of scale through virtualization and aggregated computing resources. End users, on the other hand, can reach these services through an elastic utility computing environment with minimal upfront investment. Nevertheless, pervasive use of cloud computing and the resulting rise in the number of data centers have brought forth concerns about energy consumption and carbon emission. Therefore, this paper addresses the problem of resource provisioning and task scheduling on a cloud platform under given service level agreements, in order to minimize the electric bills and maximize the profitability for the CSP. User task graphs and dependencies are randomly generated, whereas user requests for CPU and memory resources are extracted from the Google cluster trace. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and total power consumption-dependent. A negotiation-based iterative approach has been proposed for the resource provisioning and task scheduling that is inspired by a routing algorithm. More specifically, in each iteration, decisions made in the previous iteration are ripped-up and re-decided, while a congestion model is introduced to dynamically adjust the resource provisioning decisions and the schedule of each task based on the historical results as well as the current state of affairs. Experimental results demonstrate that the proposed algorithm achieves up to 63% improvement in the total electrical energy bill of an exemplary data center compared to the baseline.

Optimal offloading control for a mobile device based on a realistic battery model and semi-markov decision process

Abstract: Due to the limited battery capacity in mobile devices, the concept of mobile cloud computing (MCC) is proposed where some applications are offloaded from the local device to the cloud for higher energy efficiency. The portion of applications or tasks to be offloaded for remote processing should be judiciously determined. In this paper, the problem of optimal task dispatch, transmission, and execution in the MCC system is considered. Dynamic voltage and frequency scaling (DVFS) is applied to the local mobile processor, whereas the RF transmitter of the mobile device can choose from multiple modulation schemes and bit rates. The power consumptions of the mobile components that cannot be directly controlled, e.g., the touch screen, GPU, audio codec, and I/O ports, are also accounted for through capturing their correlation with the mobile processor and RF transmitter. Finally, a realistic and accurate battery model is adopted in this work in order to estimate the battery energy loss rate in a more accurate way. This paper presents a semi-Markov decision process (SMDP)-based optimization framework, with the actions of different DVFS levels and modulation schemes/transimission bit rates and the objective of minimizing both the energy drawn from the battery and the average latency in request servicing. This paper derives the optimal solution, including the optimal DVFS policy, offloading rate, and transmission scheme, using linear programming combined with a heuristic search. Experiments are conducted on Qualcomm Snapdragon Mobile Development Platform MSM8660 to find the correlations among the power consumptions of the CPU, RF components, and other components. Simulation results show that the proposed optimal solution consistently outperforms some baseline algorithms.

Post sign-off leakage power optimization

Abstract: With the scaling down of the CMOS technologies, leakage power is becoming an increasingly important issue in IC design. There is a trade-off between subthreshold leakage power consumption and clock frequency in the circuit; i.e., for higher performance, leakage power consumption must be sacrificed and vice versa. Meanwhile, timing analysis during synthesis and physical design is pessimistic, which means there are some slacks available to be traded for leakage power minimization. This power minimization can be done after the sign-off which is more accurate and realistic than if it is done before the sign-off. The available slack can be traded for leakage power minimization by footprint-based cell swapping and threshold voltage assignment. In this paper, we introduce our post sign-off leakage power optimization problem as a nonlinear mathematical program and solve it by using conjugate gradient (CG) method. We set up a novel transformation technique to manipulate the constraints of the optimization problem to be solved by CG. We show that by doing this optimization we can reduce the leakage power consumption by 34% on average in comparison with no power optimization after sign-off. All experiments are done on the real industrial designs.

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.