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.

B2Sim: a fast micro-architecture simulator based on basic block characterization

Abstract: State-of-the-art architectural simulators support cycle accurate pipeline execution of application programs. However, it takes days and weeks to complete the simulation of even a moderate-size program. During the execution of a program, program behavior does not change randomly but changes over time in a predictable/periodic manner. This behavior provides the opportunity to limit the use of a pipeline simulator. More precisely, this paper presents a hybrid simulation engine, named B2Sim for (cycle-characterized) Basic Block based Simulator, where a fast cache simulator e.g., sim-cache and a slow pipeline simulator e.g., sim-outorder are employed together. B2Sim reduces the runtime of architectural simulation engines by making use of the instruction behavior within executed basic blocks. We have integrated B2Sim into SimpleScalar and have achieved on average a factor of 3.3 times speedup on the SPEC2000 benchmark and Media-bench programs compared to conventional pipeline simulator while maintaining the accuracy of the simulation results with less than 1% CPI error on average.

Concurrent placement, capacity provisioning, and request flow control for a distributed cloud infrastructure

Abstract: Cloud computing and storage have attracted a lot of attention due to the ever increasing demand for reliable and cost-effective access to vast resources and services available on the Internet. Cloud services are typically hosted in a set of geographically distributed data centers, which we will call the cloud infrastructure. To minimize the total cost of ownership of this cloud infrastructure (which accounts for both the upfront capital cost and the operational cost of the infrastructure resources), the infrastructure owners/operators must do a careful planning of data center locations in the targeted service area (for example the US territories), data center capacity provisioning (i.e., the total CPU cycles per second that can be provided in each data center). In addition, they must have flow control policies that will distribute the incoming user requests to the available resources in the cloud infrastructure. This paper presents an approach for solving the unified problem of data center placement and provisioning, and request flow control in one shot. The solution technique is based on mathematical programming. Experimental results, using Google cluster data and placement/provisioning of up to eight data center sites demonstrate the cost savings of the proposed problem formulation and solution approach.

A sequential game perspective and optimization of the smart grid with distributed data centers

Abstract: The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart grid technology can incentivize the cloud computing central controller to shift the computation load towards data centers located in regions with cheaper electricity. Moreover, data centers and cloud computing also provide opportunities to help the smart grid with respect to robustness and load balancing. To gain insights into these opportunities, we consider an interaction system of the smart grid and cloud computing. We provide the sequential game formulation of the interaction system, under two different dynamic pricing scenarios: the power-dependent pricing and the time-ahead pricing. The two players in the sequential games are the smart grid controller that sets the energy price signal and the cloud computing central controller that performs resource allocation among data centers. The objective of the smart grid controller is to maximize its own profit and perform load balancing among power buses, while the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent price signal. Based on the backward induction principle, we derive the optimal or near-optimal strategies for the two players in the sequential game using convex optimization and effective heuristic search techniques. Experimental results demonstrate the effectiveness of the proposed sequential game-based optimization framework on profit maximization and load balancing.

kNN-CAM: A k-Nearest Neighbors-based Configurable Approximate Floating Point Multiplier

Abstract: In many real computations such as arithmetic operations in hidden layers of a neural network, some amounts of inaccuracies can be tolerated without degrading the final results (e.g., maintaining the same level of accuracy for image classification). This paper presents design of kNN-CAM, a k-Nearest Neighbors (kNN)-based Configurable Approximate floating point Multiplier. kNN-CAM utilizes approximate computing opportunities to deliver significant area and energy savings. A kNN engine is trained on a sufficiently large set of input data to learn the quantity of bit truncation that can be performed in each floating point input with the goal of minimizing energy and area. Next, this trained engine is used to predict the level of approximation for unseen data. Experimental results show that kNN-CAM provides about 67% area saving and 19% speedup while losing only 4.86% accuracy when compared to a 100% accurate multiplier. Furthermore, the application of kNN-CAM in implementation of a handwritten digit recognition provides 47.2% area saving while the accuracy is dropped by only 0.3%.

Crosstalk-affected delay analysis in nanometer technologies

Abstract: This article presents a detailed analysis of the crosstalk-affected delay of coupled interconnects considering process variations. We utilise a distributed RC-π model of the interconnections to accurately model process variations. In particular, we perform a detailed investigation of various crosstalk scenarios and study the impact of different parameters on crosstalk delay. Although accounting for the effect of correlations among parameters of the neighbouring wire segments, statistical properties of the crosstalk-affected propagation delays are characterised and discussed. Monte Carlo-based simulations using Spice demonstrate the effectiveness of the proposed approach in accurately modeling the correlation-aware process variations and their impact on interconnect delay in the presence of crosstalk.

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.