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.

qMC: A Formal Model Checking Verification Framework For Superconducting Logic

Abstract: Single flux quantum (SFQ) circuits as an example of superconducting electronics (SCE) have the potential to replace CMOS circuits as they possess a theoretical potential of three orders of magnitude reduction in power accompanied with one order of magnitude higher speed. Despite its benefits, the SCE community lacks a reliable open source formal verification solution. This paper proposes a verification framework called qMC, a model checker for SFQ circuits using formal techniques. qMC offers an automated process that constructs a SystemVerilog testbench consisting of formal assertions to verify the SFQ-specific properties of the circuits and produce system correctness results and counterexamples using model checking (MC). Instead of creating an MC tool from scratch, we have built qMC based on well established open source back-end verification engines for MC of CMOS circuits, including Yosys-SMTBMC and EBMC. qMC allows for properties to be given in SystemVerilog formal assertions, time-limited SystemVerilog assertions, or linear temporal logic (LTL). qMC provides an improvement in terms of verification time and coverage when compared to state-of-the-art semi-formal based SFQ verification frameworks. For instance, verification time for a 4-bit array multiplier is sped up by 19.5x.

Technology mapping and packing for coarse-grained, anti-fuse based FPGAs

Abstract: We present a new synthesis flow for anti-fuse based FPGAs with multiple-output logic cells. The flow consists of two steps: mapping and packing. The mapper finds mapping solutions using a dynamic programming-based approach that finds the best match at each node of the decomposed target circuit. After this mapping step is completed, the resulting netlist of cells in optimally packed into net list of logic cells by using a multi-dimensional coin change problem formulation which is again solved by a dynamic programming based approach. Experimental results for Quicklogic's pASIC3 logic family are provided to assess the effectiveness of the proposed mapping and packing techniques.

Fanout optimization under a submicron transistor-level delay model

Abstract: We present a new fanout optimization algorithm which is particularly suitable for digital circuits designed with submicron CMOS technologies. Restricting the class of fanout trees to the so-called bipolar LT-trees, the topology of the optimal fanout tree is found by means of a dynamic programming algorithm. The buffer selection is in turn performed by using a continuous buffer sizing technique based on a very accurate delay model especially developed for submicron CMOS processes. The fanout trees can distribute a signal with arbitrary polarity from the root of the tree to a set of sinks with arbitrary required time, required minimum signal slope, polarity and capacitive load. These trees can be constructed to maximize the required time at the root or to minimize the total buffer area under a required time constraint at the root. The performance of the algorithm shows several improvements with respect to conventional fanout optimization methods. More precisely, the area and delay improvements are 28% and 7%, respectively, when the algorithm is applied to entire circuits.

qSSTA: A Statistical Static Timing Analysis Tool for Superconducting Single-Flux-Quantum Circuits

Abstract: Superconducting single flux quantum (SFQ) technology is an ultra-high performance and low power technology The technology, however, lacks many of the design automation tools and capabilities that are commonplace in CMOS technology This article describes methods to efficiently find the conditional probability density function (PDF) of the minimum workable clock period of SFQ circuits in view of manufacturing-induced process variations and presents qSSTA, a statistical static timing analysis tool targeting SFQ circuits Following a grid-based correlation model, qSSTA represents spatial correlation of SFQ gates at different positions with respect to process parameters By approximating timing characteristics of SFQ gates in a linear model, qSSTA is able to estimate the clock period as a normal random variable Furthermore, process variations that generally result in extra delays in CMOS circuits can result in functional errors in SFQ circuits qSSTA derives the closed form of the conditional PDF of the clock period under the scenario where all SFQ gates in the circuit work correctly Compared to Monte Carlo simulations on look-up tables, experimental results show that the average percentage errors are 089% for the mean values, 804% for the standard deviation, and 061% for the 98-percentile point, whereas the runtime of qSSTA is 83% faster on average

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.