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.

Cell Delay Analysis Based on Rate-of-Current Change

Abstract: A cell delay model based on rate-of-current-change is presented, which accounts for the impact of the shape of the noisy waveform on the output voltage waveform. More precisely, a pre-characterized table of time derivatives of the output current as a function of input voltage and output load values is constructed. The data in this table, in combination with the Taylor series expansion of the output current, is utilized to progressively compute the output current waveform, which is then integrated to produce the output voltage waveform. Experimental results show the effectiveness and efficiency of this new delay model.

Hierarchical resource allocation and consolidation framework in a multi-core server cluster using a Markov decision process model

Abstract: This paper investigates a service level agreements (SLAs)-based resource allocation problem in a server cluster. The objective is to maximise the total profit, which is the total revenue minus the operational cost of the server cluster. The total revenue depends on the average request response time, whereas the operating cost depends on the total energy consumption of the server cluster. A joint optimisation framework is proposed, comprised of request dispatching, dynamic voltage and frequency scaling (DVFS) for individual cores of the servers, as well as server- and core-level consolidations. Each DVFS-enabled core in the server cluster is modelled by using a continuous-time Markov decision process (CTMDP). A near-optimal solution comprised of a central manager and distributed local agents is presented. Each local agent employs linear programming-based CTMDP solving method to solve the DVFS problem for the corresponding core. On the other hand, the central manager solves the request dispatch problem and finds the optimal number of ON cores and servers, thereby achieving a desirable tradeoff between service response time and power consumption. To reduce the computational overhead, a two-tier hierarchical solution is utilized. Experimental results demonstrate the outstanding performance of the proposed algorithm over the baseline algorithms.

High-performance and high-yield 5 nm underlapped FinFET SRAM design using P-type access transistors

Abstract: In this paper, different characteristics of SRAM cells based on 5 nm underlapped FinFET technology are studied. For the cell structures, which make use of P type access transistors and pre-discharging bitlines to “0” during the read operation, the read current and write margin (WM) are improved. In addition, 8T structures with less underlap for write access transistors are suggested. These structures may have P or N type write access transistor (denoted by 8T-P or 8T-N, respectively). In these structures, using more underlap for the pull down (pull up) transistors of the structures with the P type (N type) access transistors and doubling the fins of the write access transistor may improve the WM significantly without any adverse effect on the read SNM. The results of HSPICE simulations show about 50% improvement for the write margin. Also, the effects of the process variation on various characteristics are investigated. It is revealed that the proposed 8T-P has a WM cell sigma higher than six for supply voltages as low as 0.25 V.

A Unified Framework for System-Level Design: Modeling and Performance Optimization of Scalable Networking Systems

Abstract: The need to bring high-quality systems to market at ever increasing pace is driving the use of system-level models early in the design process. This paper presents a new unified modeling framework, called the extended queuing Petri net (EQPN), which combines extended stochastic Petri net and G/M/I queuing models, to realize the design of reliable systems during the design time, while improving the accuracy and robustness of power optimization for high-speed scalable networking systems. The EQPN model is employed to represent the performance behaviors and to minimize energy consumption of the system under performance constraints through mathematical programming formulations. Being able to model the system with the EQPN would enable the users to accomplish the design of reliable and optimized system at the beginning of design cycle. The proposed system model is compared with existing stochastic models under real simulation data. Experimental results demonstrate the effectiveness of the modeling framework and show that our proposed energy optimization techniques ensure robust system-wide energy savings under tight performance constraints

qSeq: Full Algorithmic and Tool Support for Synthesizing Sequential Circuits in Superconducting SFQ Technology

Abstract: Synthesizing general nonlinear sequential circuits in superconducting Single Flux Quantum (SFQ) technology is a challenging task involving the proper leveling of cyclic digraphs, handling nested feedback loops, and ensuring the full path balancing property throughout the synthesis process. This paper presents a precise definition of the level of a node in a cyclic digraph and a polynomial time algorithm for the corresponding level assignment and full path balancing in sequential SFQ circuits, including SFQ Finite State Machines (FSMs). A case study is conducted on a 3-bit counter, as an FSM, which has a power consumption of $44. 7 \mu W$ and $1. 4 \mu W$ using rapid SFQ and energy-efficient rapid RSFQ cells, respectively, with the local clock frequency of 55GHz (throughput of 11GHz) which is significantly higher than the typical CMOS clock frequencies. More results on larger SFQ circuits are also presented.

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.