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.

Linear-depth quantum circuits for n -qubit Toffoli gates with no ancilla

Abstract: We design a circuit structure with linear depth to implement an $n$-qubit Toffoli gate. The proposed construction uses a quadratic-size circuit that consists of elementary two-qubit controlled-rotation gates around the $x$ axis and uses no ancilla qubit. Circuit depth remains linear in quantum technologies with finite-distance interactions between qubits. The suggested construction is related to the long-standing construction by A. Barenco et al. [Phys. Rev. A 52, 3457 (1995)], which uses a quadratic-size, quadratic-depth quantum circuit for an $n$-qubit Toffoli gate.

Power efficient technology decomposition and mapping under an extended power consumption model

Abstract: We propose a new power consumption model that accounts for the power consumption at the internal nodes of a CMOS gate. Next, we address the problem of minimizing the average power consumption during the technology dependent phase of logic synthesis. Our approach consists of two steps. In the first step, we generate a NAND decomposition of an optimized Boolean network such that the sum of average switching rates for all nodes in the network is minimum. In the second step, we perform a power efficient technology mapping that finds a minimal power mapping for given timing constraints (subject to the unknown load problem). >

FinCACTI: Architectural Analysis and Modeling of Caches with Deeply-Scaled FinFET Devices

Abstract: This paper presents FinCACTI, a cache modeling tool based on CACTI which also supports deeply-scaled FinFET devices as well as more robust SRAM cells. In particular, FinFET devices optimized using advanced device simulators for 7nm process serve as the case study of the paper. Based on this 7nm FinFET process, characteristics of 6T and 8T SRAMs are calculated, and comparison results show that under the same stability requirements the 8T cell has smaller area and leakage power. SRAM and technological parameters of the 7nm FinFET are then incorporated into FinCACTI. According to architecture-level simulations, the 8T SRAM is suggested as the choice of memory cell for 7nm FinFET. Moreover, a 4MB cache in 7nm FinFET compared with 22nm (32nm) CMOS under same access latencies achieves 5x (9x) and 11x (24x) reduction in read energy and area, respectively.

Design Technologies for Low Power VLSI

Abstract: Low power has emerged as a principal theme in today’s electronics industry. The need for low power has caused a major paradigm shift where power dissipation has become as important a consideration as performance and area. This article reviews various strategies and methodologies for designing low power circuits and systems. It describes the many issues facing designers at architectural, logic, circuit and device levels and presents some of the techniques that have been proposed to overcome these difficulties. The article concludes with the future challenges that must be met to design low power, high performance systems.

Block-Based Carry Speculative Approximate Adder for Energy-Efficient Applications

Abstract: In this brief, a low energy consumption block-based carry speculative approximate adder is proposed. Its structure is based on partitioning the adder into some non-overlapped summation blocks whose structures may be selected from both the carry propagate and parallel-prefix adders. Here, the carry output of each block is speculated based on the input operands of the block itself and those of the next block. In this adder, the length of the carry chain is reduced to two blocks (worst case), where in most cases only one block is employed to calculate the carry output leading to a lower average delay. In addition, to increase the accuracy and reduce the output error rate, an error detection and recovery mechanism is proposed. The effectiveness of the proposed approximate adder is compared with state-of-the-art approximate adders using a cost function based on the energy, delay, area, and output quality. The results indicate an average of 50% reduction in terms of the cost function compared to other approximate adders.

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.