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.

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning

Abstract: Plug-in electric vehicles (PEVs) are considered the key to reducing fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology, as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This article starts with the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each hour to mitigate the effect of RS tracking error.As more and more PEVs are being plugged into the power grid, the control or management issue of PEV charging arises, since mass unregulated charging processes of PEVs may result in degradation of power quality and damage utility equipments and customer appliances. To solve this problem, this article also presents an SoH-aware charging aggregator design, which decides the control sequences of a group of PEVs. An energy storage system is used in the charging aggregator to do a peak power shaving, and future parking PEVs are properly taken care of. Experimental results show that the proposed optimal charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market. Experimental results also show that the introduction of charging aggregator can significantly reduce the peak power consumption caused by simultaneous PEV charging.

1994 international workshop on low power design

Abstract: Mr. Deo Singh, Manager of the MIPS/WATT division of Intel Corp., gave the keynote address, \"Prospects for Low Power Microprocessor Design.\" In his talk, he described the components of a modern mobile PC and their power consumption, enumerated issues relating to low power microprocessor design, packaging and power benchmarking and summarized the potential impact of voltage scaling, process scaling and low power design techniques.

HiPerRF: A Dual-Bit Dense Storage SFQ Register File

Abstract: Single Flux Quantum (SFQ) superconducting technology provides significant power and performance benefits in the era of diminishing CMOS scaling. Recent advances in design tools and fabrication facilities have brought SFQ based computing to the forefront. One challenge faced by SFQ technology is to have a compact and robust on-chip memory, which can be used for implementing register files and cache memory. While dense memories are being investigated through the development of three-terminal devices such as Nanocryotrons, in this work, we build on a novel memory cell built using traditional Josephson junctions (JJs). In particular, we design a high capacity register file, called HiPerRF, that builds on a High Capacity Destructive ReadOut (HC-DRO) cell in SFQ technology. HC-DRO design can store up to three fluxon pulses, thereby providing the equivalent of 2-bit storage in a single cell. However, these cells provide only destructive readout capability, namely each value can be read only once. However, CPU register file contents are read multiple times in any program, and hence a destructive readout complicates register file design. HiPerRF provides the non-destructive property using a loopback write mechanism, thereby preserving the higher density of HC-DRO cells without compromising the multi-read demands of a register file. HiPerRF reduces the JJ count of the register file design, after accounting for all the peripheral access circuitry costs, by 56.1% and reduces the static power by 46.2%. Furthermore, HiPerRF reduces the JJ count by 16.3% even when considering an entire in-order RISC-V CPU core.

Posit Process Element for Using in Energy-Efficient DNN Accelerators

Abstract: In this work, we present an energy-efficient posit processing element (PE) for utilization in array-based deep neural network (DNN) accelerators along with an approximation method for further reducing the energy consumption of the unit. The posit arithmetic used in the proposed PE provides high precision for the considered data widths even when approximation is used for operations. Using some modification/simplification approaches and proposing a speculative posit adder (SPA) unit, we reduce the complexity of the employed posit multiply–accumulator (MAC) in the proposed PE. The effectiveness of the proposed PE is studied using a 45-nm CMOS technology. The results reveal $3.5\times $ and 92% improvements in the delay and energy consumption, respectively, compared to those of the state-of-the-art posit PE. To assess the efficacy of the proposed PE, we have modeled an 8-bit DNN accelerator and employed it for the implementation of some DNN architectures. The results indicate that the proposed PE and its approximate one provide, on average, 19.3% and 29.6% lower energy consumptions compared to that of the latest prior work when providing 10.6% and 5.8% higher accuracies, respectively.

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.