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.

Constant-Factor Optimization of Quantum Adders on 2D Quantum Architectures

Abstract: Quantum arithmetic circuits have practical applications in various quantum algorithms. In this paper, we address quantum addition on 2-dimensional nearest-neighbor architectures based on the work presented by Choi and Van Meter (JETC 2012). To this end, we propose new circuit structures for some basic blocks in the adder, and reduce communication overhead by adding concurrency to consecutive blocks and also by parallel execution of expensive Toffoli gates. The proposed optimizations reduce total depth from $140\sqrt n+k_1$ to $92\sqrt n+k_2$ for constants $k_1,k_2$ and affect the computation fidelity considerably.

A High-Efficiency, Auto Mode-Hop, Variable-Voltage, Ripple Control Buck Converter

Abstract: In this paper, a simple yet efficient auto mode-hop ripple control structure for buck converters with light load operation enhancement is proposed. The converter, which operates under a wide range of input and output voltages, makes use of a statedependent hysteretic comparator. Depending on the output current, the converter automatically changes the operating mode. This improves the efficiency and reduces the output voltage ripple for a wide range of output currents for given input and output voltages. The sensitivity of the output voltage to the circuit elements is less than 14%, which is seven times lower than that for conventional converters. To assess the efficiency of the proposed converter, it is designed and implemented with commercially available components. The converter provides an output voltage in the range of 0.9V to 31V for load currents of up to 3A when the input voltage is in the range of 5V to 32V. Analytical design expressions which model the operation of the converter are also presented. This circuit can be implemented easily in a single chip with an external inductor and capacitor for both fixed and variable output voltage applications.

Variability-aware design of energy-delay optimal linear pipelines operating in the near-threshold regime and above

Abstract: Soft-edge flip-flop based pipelines can improve the performance and energy efficiency of circuits operating in the super-threshold (supply voltage) regime by allowing opportunistic time borrowing. The application of this technique to near-threshold regime of operation, however, faces a significant challenge due to large circuit parameter variations that result from manufacturing process imperfections and substrate temperature changes. This paper thus addresses the issue of variability-aware design of the energy-delay optimal linear pipelines that are aimed at operating in both the near-threshold and super-threshold regimes. Precisely, this goal is achieved by deriving the optimal delay line configuration in the soft-edge flip-flops in the near-threshold and the super-threshold operations regimes. The key is to ensure that the same transistor sizes result in effective operation of the delay lines (and hence appropriate settings of the transparency window size) in both operation regimes under the process induced variations. Experimental results demonstrate the efficacy of the proposed solution.

Gate sizing with controlled displacement

Abstract: In this paper, we present an algorithm for gate sizing with controlled displacement to improve the overall circuit timing. We use a path-based delay model to capture the timing constraints in the circuit. To reduce the problem size and improve the solution convergence, we iteratively identify and optimize the k-most critical paths in the circuit and their neighboring cells. All the operations are formulated and solved as mathematical programming problems by using efficient solution techniques. Experimental results on a set of benchmark circuits demonstrate the effectiveness of our approach compared to the conventional approaches, which separate gate sizing from gate placement.

QoS guaranteed online management of battery swapping station under dynamic energy pricing

Abstract: Further popularisation of electric vehicles (EVs) is hindered by their relatively short driving distance and long battery charging time. To overcome these shortcomings, the battery swapping station (BSS) has been proposed as a means of satisfying the increasing demands for fast EV battery recharging. At a BSS, (partially) depleted batteries from EVs can be replaced with partially or fully charged ones almost instantaneously. Recharging scheduling and maintenance of batteries are done by the operator of BSS, with the target of minimising electrical energy costs while satisfying customer demands. In this study, the authors consider a realistic BSS framework in which EVs can arrive at BSS with time of day dependent rates having different battery state-of-charges. They investigate the battery charging scheduling problem in the BSS under a dynamic energy pricing. They solve (i) an online optimal BSS control problem to minimise the energy cost with a quality-of-service (QoS) guarantee, and (ii) an offline optimal BSS design problem to determine the optimal number of stored batteries so as to achieve a desirable tradeoff between flexibility in charging and amortised battery costs. The experimental results show that the total charging energy cost can be reduced significantly under different traffic scenarios.

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.