Author: Xin Li Publisher: Now Publishers Inc ISBN: 1601980566 Category : Computers Languages : en Pages : 161
Book Description
Statistical Performance Modeling and Optimization reviews various statistical methodologies that have been recently developed to model, analyze and optimize performance variations at both transistor level and system level in integrated circuit (IC) design. The following topics are discussed in detail: sources of process variations, variation characterization and modeling, Monte Carlo analysis, response surface modeling, statistical timing and leakage analysis, probability distribution extraction, parametric yield estimation and robust IC optimization. These techniques provide the necessary CAD infrastructure that facilitates the bold move from deterministic, corner-based IC design toward statistical and probabilistic design. Statistical Performance Modeling and Optimization reviews and compares different statistical IC analysis and optimization techniques, and analyzes their trade-offs for practical industrial applications. It serves as a valuable reference for researchers, students and CAD practitioners.
Author: Jagdish S. Rustagi Publisher: Elsevier ISBN: 1483295710 Category : Mathematics Languages : en Pages : 376
Book Description
Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates, Markov decision processes, Programming methods used to optimize monitoring of patients in hospitals, Derivation of the Neyman-Pearson lemma, The search for optimal designs, Simulation of a steel mill. Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing Develops a wide range of statistical techniques in the unified context of optimization Discusses applications such as optimizing monitoring of patients and simulating steel mill operations Treats numerical methods and applications Includes exercises and references for each chapter Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization
Author: Ravi R. Mazumdar Publisher: Morgan & Claypool Publishers ISBN: 1627051732 Category : Computers Languages : en Pages : 213
Book Description
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in computing performance measures. The monograph also covers stochastic network theory including Markovian networks. Recent results on network utility optimization and connections to stochastic insensitivity are discussed. Also presented are ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. In particular, the important concept of effective bandwidths as mappings from queueing level phenomena to loss network models is clearly presented along with a detailed discussion of accurate approximations for large networks. Table of Contents: Introduction to Traffic Models and Analysis / Queues and Performance Analysis / Loss Models for Networks / Stochastic Networks and Insensitivity / Statistical Multiplexing
Author: Zhen Liu Publisher: Springer Science & Business Media ISBN: 0387793615 Category : Computers Languages : en Pages : 228
Book Description
With the fast development of networking and software technologies, information processing infrastructure and applications have been growing at an impressive rate in both size and complexity, to such a degree that the design and development of high performance and scalable data processing systems and networks have become an ever-challenging issue. As a result, the use of performance modeling and m- surementtechniquesas a critical step in designand developmenthas becomea c- mon practice. Research and developmenton methodologyand tools of performance modeling and performance engineering have gained further importance in order to improve the performance and scalability of these systems. Since the seminal work of A. K. Erlang almost a century ago on the mod- ing of telephone traf c, performance modeling and measurement have grown into a discipline and have been evolving both in their methodologies and in the areas in which they are applied. It is noteworthy that various mathematical techniques were brought into this eld, including in particular probability theory, stochastic processes, statistics, complex analysis, stochastic calculus, stochastic comparison, optimization, control theory, machine learning and information theory. The app- cation areas extended from telephone networks to Internet and Web applications, from computer systems to computer software, from manufacturing systems to s- ply chain, from call centers to workforce management.
Author: Archana Ganapathi Publisher: ISBN: Category : Languages : en Pages : 218
Book Description
The complexity of modern computer systems makes performance modeling an invaluable resource for guiding crucial decisions such as workload management, configuration management, and resource provisioning. With continually evolving systems, it is difficult to obtain ground truth about system behavior. Moreover, system management policies must adapt to changes in workload and configuration to continue making efficient decisions. Thus, we require data-driven modeling techniques that auto-extract relationships between a system's input workload, its configuration parameters, and consequent performance. This dissertation argues that statistical machine learning (SML) techniques are a powerful asset to system performance modeling. We present an SML-based methodology that extracts correlations between a workload's pre-execution characteristics or configuration parameters, and post-execution performance observations. We leverage these correlations for performance prediction and optimization. We present three success stories that validate the usefulness of our methodology on storage and compute based parallel systems. In all three scenarios, we outperform state- of-the-art alternatives. Our results strongly suggest the use of SML-based performance modeling to improve the quality of system management decisions.
Author: M.H.A. Davis Publisher: Routledge ISBN: 1351433490 Category : Mathematics Languages : en Pages : 308
Book Description
This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process that follows deterministic trajectories between random jumps, the latter occurring either spontaneously, in a Poisson-like fashion, or when the process hits the boundary of its state space. This formulation includes an enormous variety of applied problems in engineering, operations research, management science and economics as special cases; examples include queueing systems, stochastic scheduling, inventory control, resource allocation problems, optimal planning of production or exploitation of renewable or non-renewable resources, insurance analysis, fault detection in process systems, and tracking of maneuvering targets, among many others. The first part of the book shows how these applications lead to the PDP as a system model, and the main properties of PDPs are derived. There is particular emphasis on the so-called extended generator of the process, which gives a general method for calculating expectations and distributions of system performance functions. The second half of the book is devoted to control theory for PDPs, with a view to controlling PDP models for optimal performance: characterizations are obtained of optimal strategies both for continuously-acting controllers and for control by intervention (impulse control). Throughout the book, modern methods of stochastic analysis are used, but all the necessary theory is developed from scratch and presented in a self-contained way. The book will be useful to engineers and scientists in the application areas as well as to mathematicians interested in applications of stochastic analysis.
Author: Geoffrey Vining Publisher: CRC Press ISBN: 1482276763 Category : Business & Economics Languages : en Pages : 504
Book Description
Demonstrates ways to track industrial processes and performance, integrating related areas such as engineering process control, statistical reasoning in TQM, robust parameter design, control charts, multivariate process monitoring, capability indices, experimental design, empirical model building, and process optimization. The book covers a range o
Author: B. Everitt Publisher: Springer Science & Business Media ISBN: 9400931530 Category : Science Languages : en Pages : 87
Book Description
Optimization techniques are used to find the values of a set of parameters which maximize or minimize some objective function of interest. Such methods have become of great importance in statistics for estimation, model fitting, etc. This text attempts to give a brief introduction to optimization methods and their use in several important areas of statistics. It does not pretend to provide either a complete treatment of optimization techniques or a comprehensive review of their application in statistics; such a review would, of course, require a volume several orders of magnitude larger than this since almost every issue of every statistics journal contains one or other paper which involves the application of an optimization method. It is hoped that the text will be useful to students on applied statistics courses and to researchers needing to use optimization techniques in a statistical context. Lastly, my thanks are due to Bertha Lakey for typing the manuscript.
Author: Eslam Yahya Publisher: Springer ISBN: 9781461405443 Category : Technology & Engineering Languages : en Pages : 200
Book Description
Asynchronous circuits provide efficient solutions for many of the recent nanometric technologies, although the lack of analysis and optimization tools has limited the commercial spread of this technology. This book helps readers to understand the difficulties of modeling and analyzing asynchronous circuits. A new modeling methodology is introduced; it is used to build Asynchronous Static Timing Analysis (ASTA) and Asynchronous Statistical Static Timing Analysis (ASSTA). These fast and accurate methods are used to optimize the circuit speed against its hardware size. In addition, the book investigates the handshaking protocol effect on different asynchronous circuit performance metrics (speed, power consumption, EMI and robustness against process variability). The book is accompanied by AHMOSE (Asynchronous High-speed Modeling and Optimization Tool-set), a demonstrative software, which provides to the user a better understanding and usage of the explained methods.
Author: Shubhankar Basu Publisher: ISBN: Category : Languages : en Pages : 215
Book Description
As semiconductor industry continues to follow Moore's Law of doubled device count every 18 months, it is challenged by the rising uncertainties in the manufacturing process for nanometer technologies. Manufacturing defects lead to a random variation in physical parameters like the dopant density, critical dimensions and oxide thickness. These physical defects manifest themselves as variations in device process parameters like threshold voltage and effective channel length of transistors. The randomness in process parameters affect the performance of VLSI circuits which leads to a loss in parametric yield. Conventional design methodologies, with corner case based analysis techniques fail to predict the performance of circuits reliably in the presence of random process variations. Moreover, the analysis techniques for detection of defects in the later stages of the design cycle result in significant overhead in cost due to re-spins. In recent times, VLSI computer aided design methodologies have shifted to statistical analysis techniques for performance measurements with specific yield targets. However, the adoption of statistical techniques in commercial design flows has been limited by the complexity of their usage and the need for generating specially characterized models. This also makes them unsuitable in repeated loops during the synthesis process. In this dissertation, we present an alternate approach to model and optimize the performance of digital and analog circuits in the presence of random process variations. Our work is targeted for a bottom-up methodology providing incremental tolerance to the circuits under the impact of random process variations. The methodologies presented, can be used to generate fast evaluating accurate macromodels to compute the bounds of performance due to the underlying variations in device parameters. The primary goal of our methodology is to capture the statistical aspects of variation in the lower levels of abstraction, while aiding deterministic analysis during the top level design optimization. We also attempt to build our solutions as a wrapper around a conventional design flow, without the requirement for special characterization. The modeling and optimization techniques are perfectly scalable across technology generations and can find practical usage during variation-tolerant synthesis of VLSI circuit performance.
Author: Xin Li
Author: Jagdish S. Rustagi
Author: Ravi R. Mazumdar
Author: Zhen Liu
Author: Archana Ganapathi
Author: M.H.A. Davis
Author: Geoffrey Vining
Author: B. Everitt
Author: Eslam Yahya
Author: Shubhankar Basu