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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.


Papers
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Proceedings ArticleDOI
27 Aug 2015
TL;DR: This work proposes to model the HEV power management problem as a Markov decision process (MDP) and derives the optimal power management policy using the policy iteration technique and demonstrates that the proposed optimalpower management policy improves HEV fuel economy by 23.9% on average compared to the rule-based policy.
Abstract: In contrast to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollutant emissions. The HEV features a hybrid propulsion system consisting of one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and so advanced power management policy is required for achieving higher performance and lower fuel consumption. This work aims at minimizing the HEV fuel consumption over any driving cycles, about which no complete information is available to the HEV controller in advance. Therefore, this work proposes to model the HEV power management problem as a Markov decision process (MDP) and derives the optimal power management policy using the policy iteration technique. Simulation results over real-world and testing driving cycles demonstrate that the proposed optimal power management policy improves HEV fuel economy by 23.9% on average compared to the rule-based policy.

9 citations

Journal ArticleDOI
TL;DR: This paper provides a systematic solution for the single-source, single-destination charge migration problem considering the efficiency variation of the converters, the rate capacity and internal power loss of the storage element, the terminal voltage variation ofThe storage elements as a function of their state of charge, and so on.
Abstract: In spite of extensive research it is still quite expensive to store electrical energy without converting it to a different form of energy. As of today, no single type of electrical energy storage (EES) element can fulfill all the desirable features of an ideal storage device, e.g., high-efficiency, high-power/energy capacity, low-cost, and long-cycle life. A hybrid EES system (HEES) consists of two or more heterogeneous EES elements, realizing the advantages of each EES element while hiding their weaknesses. HEES systems exhibit superior performance compared with homogeneous EES systems when appropriate charge allocation and replacement policies are developed and used. In addition, charge migration is mandatory because the optimal EES banks for charge allocation and replacement are in general different, and each EES bank has limited storage capacity. This paper formally describes the notion of charge migration efficiency and its optimization. We first define the charge migration architecture and the corresponding charge migration optimization problem. We provide a systematic solution for the single-source, single-destination charge migration problem considering the efficiency variation of the converters, the rate capacity and internal power loss of the storage element, the terminal voltage variation of the storage elements as a function of their state of charge, and so on. We also introduce the optimal solutions for both the time-constrained and -unconstrained versions of the charge migration problem formulations. Experimental results demonstrate significant charge migration efficiency improvement of up to 83.4%.

8 citations

Journal ArticleDOI
TL;DR: This paper first presents grid-connected dual-bank HEES system design and management to maximize the electric bill savings for residential users, and subsequently provides a comprehensive sensitivity analysis of the economic feasibility of residential HEES systems.
Abstract: Hybrid electrical energy storage (HEES) systems have the potential to result in considerable cost savings by reducing the electric bills of home users. This paper first presents grid-connected dual-bank HEES system design and management to maximize the electric bill savings for residential users, and subsequently provides a comprehensive sensitivity analysis of the economic feasibility of residential HEES systems. Specifically, the paper describes a daily management policy based on energy buffering strategy with one bank as the main storage bank and the other as the energy buffering bank, and then derive the global design of HEES specifications based on the daily management results. Simulation results prove the effectiveness of energy buffering strategy and show the proposed HEES system is capable of bringing in profits under current input variables. Finally, a detailed analysis is conducted to show how each input variable affects the final design of the proposed residential HEES system and the maximum annual profits it achieves. Together with the design and control mechanism, the proposed analysis provides potential customers with the comprehensive knowledge of how HEES systems can be deployed to achieve savings in their electric bills.

8 citations

Proceedings ArticleDOI
25 Mar 2020
TL;DR: A low-power accuracy-configurable block-based Carry Look-ahead Adder that employs the voltage over scaling and number of approximate blocks as the approximation knobs for improving the energy consumption as well as the reliability and lifetime of the adder.
Abstract: In this paper, a low-power accuracy-configurable block-based Carry Look-ahead Adder (AC-CLA) is proposed. The structure employs the voltage over scaling and number of approximate blocks as the approximation knobs for improving the energy consumption as well as the reliability and lifetime of the adder. While the former may be set in the design time as well as the runtime, the latter may only be invoked in the design time. In this adder, for a given accuracy level, some of the blocks work in the approximate mode by using over-scaled voltages. The block-based structure enables applying the overscaled voltage for each block independently. The efficacy of the adder depends on the number of the approximate blocks as well as the VOS voltage levels used for these blocks. The use of lower VOS voltage levels for the blocks responsible for lower significant bits which have higher switching activities is the key for reducing the power consumption of the adder while having the error within a tolerable limit. The structure requires few level shifters making the realization overhead low. The efficiency of the AC-CLA structure is studied using a 15 nm FinFET technology. The results of the study indicate that in the approximate mode up to 57% energy saving may be achieved. In addition, for this adder, the BTI induced delay degradation of the adder over 10 years decreases by up to 7% compared to 50% in the case of the exact operating mode. Finally, the efficacy of AC-CLA adder is assessed in a neural network for the classification application.

8 citations

Proceedings ArticleDOI
15 Mar 2016
TL;DR: Experimental data shows that the proposed framework not only provides accurate results in timing analysis, but also can capture the effect of arbitrary voltage noise.
Abstract: Accurate timing analysis is a critical step in the design of VLSI circuits. In addition, nanoscale FinFET devices are emerging as the transistor of choice in 32nm CMOS technologies and beyond. This is due to their more effective channel control, higher ON/OFF current ratios, and lower energy consumption. In this paper, an efficient Current Source Model (CSM) is presented to calculate the output waveform as well as the read/write delay of 6T FinFET SRAM cells accounting for noisy waveform at each voltage node. In this model, the non-linear analytical methods and low-dimensional CSM lookup tables (LUTs) are combined to simultaneously achieve high modeling accuracy and time/space efficiency. Experimental data shows that our proposed framework not only provides accurate results in timing analysis, but also can capture the effect of arbitrary voltage noise.

8 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations