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.

Timing Characterization for Static Timing Analysis of Single Flux Quantum Circuits

Abstract: Single flux quantum (SFQ) logic families require the development of electronic design automation tools to generate large-scale circuits. The available methodologies or tools for performing timing analysis of SFQ circuits do not have a load-dependent timing characterization method for calculating the context-dependent delay of cells, such as the nonlinear delay model for complementary metal–oxide–semiconductor (CMOS) circuits. A new timing characterization method is presented here for SFQ logic cells, which relies on low-dimensional lookup tables (LUTs) to store the clock-to-output delay, setup, and hold times of clocked cells and input-to-output delay of nonclocked cells in an SFQ standard cell library. Although the delay of Josephson junction based logic cells depends on many parameters, this paper shows that it is possible to reduce this dependency to only a small number of well-chosen parameters. All LUTs are obtained from JSIM simulations for a given target process technology. The accuracy of the proposed LUT-based timing characterization method is compared against JSIM simulations, which shows a maximum error of only 2.1% of the tested clocked cells with different loads.

Designing a residential hybrid electrical energy storage system based on the energy buffering strategy

Abstract: Due to severe variation in load demand over time, utility companies generally raise electrical energy price during periods of high load demand. A grid-connected hybrid electrical energy storage (HEES) system can help residential users lower their electric bills by storing energy during low-price hours and releasing the stored energy during high-price hours. A HEES system consists of different types of electrical energy storage (EES) elements, utilizing the benefits of each type while hiding their weaknesses. This paper presents a residential energy management system to maximize the annual profits on residential electric bills, based on a HEES system comprised of a lead-acid battery bank as the main storage bank and a Li-ion battery bank as the energy buffer. We first derive the optimal daily energy management policy based on energy buffering to minimize the daily energy cost. Next, we find the near-optimal design specifications of the energy management system, aiming at maximizing the amortized annual profits under practical constraints. We show that this system achieves averagely 11.10% more profits compared to the none-buffering HEES system.

Non-Gaussian Statistical Interconnect Timing Analysis

Abstract: This paper focuses on statistical interconnect timing analysis in a parameterized block-based statistical static timing analysis tool. In particular, a new framework for performing timing analysis of RLC networks with step inputs, under both Gaussian and non-Gaussian sources of variation, is presented. In this framework, resistance, inductance, and capacitance of the RLC line are modeled in a canonical first order form and used to produce the corresponding propagation delay and slew (time) in the canonical first-order form. To accomplish this step, mean, variance, and skewness of delay and slew distributions are obtained in an efficient, yet accurate, manner. The proposed framework can be extended to consider higher order terms of the various sources of variation. Experimental results show average errors of less than 2% for the mean, variance and skewness of interconnect delay and slew while achieving orders of magnitude speedup with respect to a Monte Carlo simulation with 104 samples.

Circuit and System Level Power Management

Abstract: This chapter describes the concept of dynamic power management (DPM), which is a methodology used to decrease the power consumption of a system. In DPM, a system is dynamically reconfigured to lower the power consumption while meeting some performance requirement. In other words, depending on the necessary performance and the actual computation load, the system or some of its blocks are tuned-off or their performance is lowered. This chapter reviews several approaches to system-level DPM, including fixed time-out, predictive shut-down or wake-up, and stochastic methods. In addition, it presents the key ideas behind circuit-level power management including clock gating, power gating and precomputation logic. The chapter concludes with a description of several runtime mechanisms for leakage power control in VLSI circuits.

VeriSFQ: A Semi-formal Verification Framework and Benchmark for Single Flux Quantum Technology

Abstract: In this paper, we propose a semi-formal verification framework for single-flux quantum (SFQ) circuits called VeriSFQ, using the Universal Verification Methodology (UVM) standard. The considered SFQ technology is superconducting digital electronic devices that operate at cryogenic temperatures with active circuit elements called the Josephson junction, which operate at high switching speeds and low switching energy - allowing SFQ circuits to operate at frequencies over 300 gigahertz. Due to key differences between SFQ and CMOS logic, verification techniques for the former are not as advanced as the latter. Thus, it is crucial to develop efficient verification techniques as the complexity of SFQ circuits scales. The VeriSFQ framework focuses on verifying the key circuit and gate-level properties of $\mathrm{SFQ}$ logic: fanout, gate-level pipeline, path balancing, and input-to-output latency. The combinational circuits considered in analyzing the performance of VeriSFQ are: Kogge-Stone adders (KSA), array multipliers, integer dividers, and select ISCAS’85 combinational benchmark circuits. Methods of introducing bugs into SFQ circuit designs for verification detection were experimented with - including stuck-at faults, fanout errors, unbalanced paths, and functional bugs like incorrect logic gates. In addition, we propose an SFQ verification benchmark consisting of combinational SFQ circuits that exemplify SFQ logic properties and present the performance of the VeriSFQ framework on these benchmark circuits. The portability and reusability of the UVM standard allows the VeriSFQ framework to serve as a foundation for future SFQ semi-formal verification techniques.

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.