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.

Architecture and routability analysis for row-based FPGAs

Abstract: FPGAs combine the logic integration benefits of custom VLSI with the design, production, and time-to-market advantages of standard logic ICs. One class of FPGAs has rows of logic cells interspersed with routing channels have given this family of FPGA devices the flavor of traditional channeled gate arrays or standard cells. This class has the flavor of traditional channeled gate arrays or standard cells and is exemplified by the Actel family of FPGAs. However, unlike conventional standard cell designs, the FPGA routing channels contain predefined wiring segments of various lengths which may be interconnected using antifuses. This paper develops analytical models that permit the design of FPGA routing channels and the analysis of the routability of row-based FPGAs devices based on a generic characterization of the row-based FPGA routing algorithms. In particular, it demonstrates that (using probabilistic models for the origination point and length for connections) an FPGA with properly designed segment length and distribution can be nearly as efficient as a mask-programmable channel (in terms of number of tracks required for routing a given interconnection specification). Experimental results corroborate this prediction. In addition, this paper provides a method for evaluating various channel architectures.

TDP-ADMM: A Timing Driven Placement Approach for Superconductive Electronic Circuits Using Alternating Direction Method of Multipliers

Abstract: This paper presents a novel timing driven global placement approach utilizing the alternating direction method of multipliers (ADMM) targeting superconductive electronic circuits. The proposed algorithm models the placement problem as an optimization problem with constraints on the maximum wirelength delay of timing-critical paths and employs the ADMM algorithm to decompose the problem into two sub-problems, one minimizing the total wirelength of the circuit and the other minimizing the delay of timing-critical paths of the circuit. Through an iterative process, a placement solution is generated that simultaneously minimizes the total wirelength and satisfies the setup time constraints. Compared to an state-of-the-art academic global placement tool, the proposed method (called TDP-ADMM) improves the worst and total negative slack for seven single flux quantum benchmark circuits by an average of 26% and 44%, respectively, with an average overhead of 1.98% in terms of total wirelength.

A timing uncertainty-aware clock tree topology generation algorithm for single flux quantum circuits

Abstract: This paper presents a low-cost, timing uncertainty-aware synchronous clock tree topology generation algorithm for single flux quantum (SFQ) logic circuits. The proposed method considers the criticality of the data paths in terms of timing slacks as well as the total wirelength of the clock tree and generates a (height-) balanced binary clock tree using a bottom-up approach and an integer linear programming (ILP) formulation. The statistical timing analysis results for ten benchmark circuits show that the proposed method improves the total wirelength and the total negative hold slack by 4.2% and 64.6%, respectively, on average, compared with a wirelength-driven state-of-the-art balanced topology generation approach.

Power minimisation techniques at the RT-level and below

Abstract: This chapter reviewed a number of RTL techniques for low power design of VLSI circuits targeting both dynamic and leakage components of power dissipation in CMOS VLSI circuits. A more detailed review of techniques for low power design of VLSI circuits and systems can be found in many references, including Reference 1.

PLATO P: PLA timing optimization by partitioning

Abstract: This paper addresses the problem of partitioning a large PLA into a number of smaller PLA's (sub-PLA's) such that the total area of these sub-PLA's is minimum and the cycle time of the partitioned circuit is minimized. First, we describe an iterative improvement method that deals with the case that sub-PLA's assume arbitrary sizes. Second, we present a partitioning technique based on fuzzy logic that deals with the case that the sizes of the sub-PLA's are fixed. Finally, we describe a method that considers not only delay through each sub-PLA, but also loading of sub-PLA's on the stage that is driving them. These techniques have been implemented and significantly outperformed conventional PLA partitioning schemes.

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.