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.

ColdFlux Superconducting EDA and TCAD Tools Project: Overview and Progress

Abstract: The IARPA SuperTools program requires the development of superconducting electronic design automation (S-EDA) and superconducting technology computer-aided design (S-TCAD) tools aimed at enabling the reliable design of complex superconducting digital circuits with millions of Josephson junctions. Within the SuperTools program, the ColdFlux project addresses S-EDA and S-TCAD tool research and development in four areas: 1) RTL synthesis, architectures and verification; 2) analog design and layout synthesis; 3) physical design and test; and 4) device and process modeling/simulation and cell library design. Capabilities include, but are not limited to, the following: device level modeling and simulation of Josephson junctions, modeling and simulation of the superconducting process manufacturing processes, powerful new electrical circuit simulation, parameterized schematic and layout libraries, optimization, compact SPICE-like model extraction, timing analysis, behavioral, register-transfer-level and logic syntheses, clock tree synthesis, placement and routing, layout-versus-schematic extraction, functional verification, and the evaluation of designs in the presence of magnetic fields and trapped flux. ColdFlux consists of six research groups from four continents. Here, we present an overview of the current and planned activities related to the project and justify the design assumptions and decisions that were made to allow the development of design tools for million-gate circuits.

Layout Optimization for Quantum Circuits with Linear Nearest Neighbor Architectures

Abstract: This paper is concerned with the physical design of quantum logic circuits. More precisely, it addresses the problem of minimizing the number of required qubit reorderings (achieved by inserting explicit SWAP gates) when mapping a quantum circuit into a linear nearest neighbor quantum archi-tecture. First, an interaction graph that captures the interaction distances among various qubits in the quantum circuit is constructed. The interaction graph is utilized to partition the quantum circuit into a set of subcircuits such that the number of required qubit reoderings within each subcircuit is provably no more than a given threshold. Next, a Minimum Linear Arrangement problem for each subcircuit is formulated and solved to achieve the minimum number of internal qubit reorderings and determine the subcircuit input and output qubit orderings. Finally, a bubble sort algorithm is repeatedly employed to minimize the number of qubit reorderings that are required between the consecutive subcircuits. Experiments done on various quantum Fourier transform circuits as well as various reversible logic circuits demonstrate the effectiveness of the proposed approach.

Approximate Reverse Carry Propagate Adder for Energy-Efficient DSP Applications

Abstract: In this paper, a reverse carry propagate adder (RCPA) is presented. In the RCPA structure, the carry signal propagates in a counter-flow manner from the most significant bit to the least significant bit; hence, the carry input signal has higher significance than the output carry. This method of carry propagation leads to higher stability in the presence of delay variations. Three implementations of the reverse carry propagate full-adder (RCPFA) cell with different delay, power, energy, and accuracy levels are introduced. The proposed structure may be combined with an exact (forward) carry adder to form hybrid adders with tunable levels of accuracy. The design parameters of the proposed RCPA implementations and some hybrid adders realized utilizing these structures are studied and compared with those of the state-of-the-art approximate adders using HSPICE simulations in a 45-nm CMOS technology. The results indicate that employing the proposed RCPAs in the hybrid adders may provide, on average, 27%, 6%, and 31% improvements in delay, energy, and energy-delay-product while providing higher levels of accuracy. In addition, the structure is more resilient to delay variation compared to the conventional approximate adder. Finally, the efficacy of the proposed RCPAs is investigated in the discrete cosine transform (DCT) block of the JPEG compression and finite-impulse response (FIR) filter applications. The investigation reveals 60% and 39% energy saving in the DCT of JPEG and FIR filter, respectively, for the proposed RCPAs.

Architecture and Control Algorithms for Combating Partial Shading in Photovoltaic Systems

Abstract: Partial shading is a serious obstacle to the effective utilization of photovoltaic (PV) systems since it can result in a significant degradation in the PV system output power. A PV system is organized as a series connection of PV modules, each module comprising a number of series-parallel connected PV cells. Backup PV cell employment and PV module reconfiguration techniques have been proposed to improve the performance of the PV system under the partial shading effects. However, these approaches are not very effective since they are costly in terms of their PV cell count and/or cell connectivity requirements. In contrast, this paper presents a cost-effective, reconfigurable PV module architecture with integrated switches in each PV cell. This paper also presents a dynamic programming algorithm to adaptively produce near-optimal reconfigurations of each PV module so as to maximize the PV system output power under any partial shading pattern. We implement a working prototype of reconfigurable PV module with 16 PV cells and confirm 45.2% output power level improvement. Using accurate PV cell models extracted from prototype measurement, we have demonstrated up to a factor of 2.36X output power improvement of a large-scale PV system comprised of three PV modules with 60 PV cells per module.

Effects of non-uniform substrate temperature on the clock signal integrity in high performance designs

Abstract: This paper presents the analysis and modeling of the nonuniform substrate temperature in high performance ICs and its effect on the integrity of the clock signal. Using a novel non-uniform temperature-dependent distributed RC interconnect delay model, the behavior of clock skew in the presence of the substrate thermal gradients is analyzed and some design guidelines are provided to ensure the integrity of the clock signal.

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.