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.

A cross-layer framework for designing and optimizing deeply-scaled FinFET-based SRAM cells under process variations

Abstract: A cross-layer framework (spanning device and circuit levels) is presented for designing robust and energy-efficient SRAM cells, made of deeply-scaled FinFET devices. In particular, 7nm FinFET devices are designed and simulated by using Synopsys TCAD tool suite, Sentaurus. Next, 6T and 8T SRAM cells, which are composed of these devices, are designed and optimized. To enhance the cell stability and reduce leakage energy consumption, the dual (i.e., front and back) gate control feature of FinFETs is exploited. This is, however, done without requiring any external signal to drive the back gates of the FinFET devices. Subsequently, the effect of process variations on the aforesaid SRAMs is investigated and steps are presented to protect the cells against these variations. More precisely, the SRAM cells are first designed to minimize the expected energy consumption (per clock cycle) subject to the non-destructive read and successful write requirements under worst-case process corner conditions. These SRAM cells, which are overly pessimistic, are then refined by selectively adjusting some transistor sizes, which in turn reduces the expected energy consumption while ensuring that the parametric yield of the cells remains above some prespecified threshold. To do this efficiently, an analytical method for estimating the yield of SRAM cells under process variations is also presented and integrated in the refinement procedure. A dual-gate controlled 6T SRAM cell operating at 324mV (in the near-threshold supply regime) is finally presented as a high-yield and energy-efficient memory cell in the 7nm FinFET technology.

Buffered Routing Tree Construction Under Buffer Placement Blockages

Abstract: Interconnect delay has become a critical factor in determining the performance of integrated circuits. Routing and buffering are powerful approaches to improve circuit speed and correct timing violations after global placement. This paper presents a dynamic-programming based algorithm for performing net topology construction and buffer insertion and sizing simultaneously under the given buffer-placement blockages. The differences from some previous works are that (1) the buffer locations are not pre-determined, (2) the multi-pin nets are easily handled, and (3) a line-search routing algorithm is implemented to speed up the process. Heuristics are used to reduce the problem complexity, which include limiting number of intermediate solutions, using a continuous buffer sizing model, and restricting the buffer locations along the Hanan graph. The resulting algorithm, named BRBP, was applied to a number of industrial designs and achieved an average of 7.9% delay improvement compared to a conventional design flow.

IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS Editor-in-Chief

Abstract: The purpose of this special issue is to engage the engineering and scientific communities, particularly the IEEE, in the emerging ‘Organic Electronics’, also commonly known as ‘Printed Electronics’ (PE). The special issue is intended to delineate the state-of-the-art and challenges facing the PE industry, as well as, to present the current activities, solutions, and future work of researchers, both from academia and industry, in this emerging field. Another purpose is to connect the different disciplines embodied in PE, with emphasis on their implications to circuit design – from a circuits and systems perspective. Broadly, PE encompasses five supply chains: (i) Materials; (ii) Processing Equipment/Platforms; (iii) Circuits/Power Source/Display/Memory/Sensors; (iv) System Integration; and (v) Test and Verification. The scope of this special issue virtually covers all these chains with focus on how they, individually or collectively, affect the printed elements and hence the ensuing circuits and systems. Of specific interest, the scope includes the co-design of the different chains with the third chain (Circuits/Power Source/Display/Memory/Sensors); particularly how innovative circuits and systems design may be able to circumvent or at least mitigate the formidable challenges and shortcomings of PE. Both PE-only (fully-printed) and ‘Hybrid Electronics’ (embodying a heterogeneous integration of conventional silicon transistors with printed circuit elements) on flexible substrate, such as PET plastic films, are within the scope of this special issue. However, there is emphasis for full realizations on flexible substrates (PE-only) as this significantly broadens the application space of PE, for instance, as a key technological enabler for the Internet-of-Things. Put simply, the overall scope encompasses all aspects of the multi-disciplinary PE with emphasis in a circuits and systems perspective and includes: a. Provide prevailing and open problems of PE to the engineering and scientific communities, including the circuits and systems and solid-state design communities;

Context-driven power management in cache-enabled base stations using a Bayesian neural network

Abstract: Aggressive network densification in next generation cellular networks is accompanied by an increase of the system energy consumption and calls for more advanced power management techniques in base stations. In this paper, we present a novel proactive and decentralized power management method for small cell base stations in a cache-enabled multitier heterogeneous cellular network. User contexts are utilized to drive the decision of dynamically switching a small cell base station between the active mode and the sleep mode to minimize the total energy consumption. The online control problem is formulated as a contextual multi-armed bandit problem. A variational inference based Bayesian neural network is proposed as the solution method, which implicitly finds a proper balance between exploration and exploitation. Experimental results show that the proposed solution can achieve up to 46.9% total energy reduction compared to baseline algorithms in the high density deployment scenario and has comparable performance to an offline optimal solution.

High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis

Abstract: Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are capable of tracking changes in underlying distributions of input features. This intrinsically slow convergence of adaptive methods combined with existing hardware implementations that operate at very low clock frequencies necessitate fundamental improvements in both algorithm and hardware design. This paper presents an algorithm that allows efficient hardware implementation of ICA. Compared to previous work, our FPGA implementation of adaptive ICA improves clock frequency by at least one order of magnitude and throughput by at least two orders of magnitude. Our proposed algorithm is not limited to ICA and can be used in various machine learning problems that use stochastic gradient descent optimization.

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.