Adaptive Approximate Computing For Enhanced Quality Assurance
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Adaptive Approximate Computing For Enhanced Quality Assurance
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Author : Mahmoud Saleh Masadeh
language : en
Publisher:
Release Date : 2022
Adaptive Approximate Computing For Enhanced Quality Assurance written by Mahmoud Saleh Masadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Approximate Computing (AC) has been widely advocated for energy-efficiency in error-tolerant applications as it offers the opportunity to trade-off output quality for reduced power consumption and execution time. Approximate accelerators, which consist of a large number of functional units, have been proposed in error-resilient applications, to speed-up regularly executed code elements while assuring defined quality constraints. However, with an approximate static design, while the average output quality constraint is satisfied, the quality of individual outputs varies significantly with dynamically changing inputs. Thus, quality assurance is an essential and non-trivial problem. State-of-the-art approaches in approximate computing address this problem by precisely re-evaluating those quality-violating accelerator invocations. However, such methods can significantly diminish or even cancel the benefits of approximation, especially when the rate of input data variations is high and approximate errors are considerably above a user given threshold, i.e., target output quality (TOQ). As a general solution to this problem, in this thesis, we propose a novel methodology to enhance the quality of approximation by two approaches: 1) design adaptation by predicting the most suitable settings of the approximate design to execute the inputs; and/or 2) error compensation by predicting the error magnitude to use in adjusting the output results. The proposed method predicts the design settings, or the error magnitude based on the applied input data and user preferences, without losing the gains of approximations. We mostly consider the case of approximate accelerators built with approximate functional units such as approximate multipliers, where we design a library of approximate accelerators with 20 different settings of 8 and 16-bit approximate multipliers. For the adaptive approximate computing, we use machine learning (ML) algorithms to build an efficient and lightweight design selector to adapt the approximate accelerators to meet a user-defined quality constraint. Compared with contemporary techniques, our approach is a fine-grained input-dependent approximation approach, with no missed approximation opportunities or rollback recovery overhead. The proposed method applies to any approximate accelerator with error-tolerant components, and it is flexible in adapting various error metrics. We fully automate the proposed methodology of quality assurance of approximate accelerators using ML-based models, for both software and hardware implementations. The obtained analysis results of image processing and audio applications showed that it is possible to satisfy the TOQ with an accuracy ranging from 80% to 85.7%. The hardware implementation is based on Field Programmable Gate Arrays (FPGA) approximate adaptive accelerator with constraints on size, cost, and power consumption, which rely on dynamic partial reconfiguration to assist in satisfying these requirements. To ensure the quality of results for a single approximate design rather than a library, we build a decision tree-based model for error compensation. The proposed model detects the magnitude of approximation error based on design inputs. Then, it enhances the accuracy of the approximate result by adding the error magnitude to it. The proposed methodology is able to enhance the quality of image processing applications with a negligible overhead.
Design And Applications Of Emerging Computer Systems
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Author : Weiqiang Liu
language : en
Publisher: Springer Nature
Release Date :
Design And Applications Of Emerging Computer Systems written by Weiqiang Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Introducing Principled Approximation And Online Control Into Streaming Applications
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Author : Yan Pei (Ph. D.)
language : en
Publisher:
Release Date : 2021
Introducing Principled Approximation And Online Control Into Streaming Applications written by Yan Pei (Ph. D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The ubiquity of streaming applications in important domains such as deep learning, computer vision/graphics, Internet of Things has opened up opportunities for the use of approximate computing to enable efficient execution of these applications on a wide range of platforms. This dissertation explores the use of ideas from machine learning and control theory to exploit approximation in streaming applications in a principled way. We first present online control techniques of introducing principled approximation into Simultaneous Localization and Mapping (SLAM) algorithms, which are used in emerging domains like robotics and autonomous driving in which autonomous agents build a map while navigating through unknown environments. Existing studies of approximation in SLAM have mostly used offline control, assuming the trajectory is known before the agent starts to move, which is impractical. The proposed methodology controls approximation in an adaptive manner without causing unacceptable quality degradation, enabling efficient deployment of SLAM on a wider range of resource-constrained platforms. We also propose Sonic, a sampling-based online controller for general constrained optimization problems in long-running streaming applications. Within each phase of a streaming application’s execution, Sonic utilizes the beginning portion to sample the knob space sequentially and aims to pick the optimal knob setting for the rest of the phase, given a user-specified constrained optimization problem. Machine learning regressors and Bayesian optimization are applied in Sonic for better sampling choices. Our experiments show that Sonic is able to find near-optimal knob settings at run time for the applications we studied. For online control, state estimation is a key component. In this dissertation, we introduce a novel derivation of Kalman filtering, a classic state estimation technique that can be used in online control to combine noisy estimates of quantity of interest. It is presented from an abstract perspective with key assumptions and concepts clarified. We then exploit these insights and propose RASR, a performance-oriented super-resolution program for rendered content that takes advantage of internal sub-pixel states of graphics hardware. RASR is a deep learning approach to fusing frames of low-fidelity into one highfidelity frame, designed to meet the increasing demand for high throughput, high quality and low latency in real-time rendering
Computational Science Iccs 2007
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Author : Yong Shi
language : en
Publisher: Springer
Release Date : 2007-07-14
Computational Science Iccs 2007 written by Yong Shi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-14 with Computers categories.
Part of a four-volume set, this book constitutes the refereed proceedings of the 7th International Conference on Computational Science, ICCS 2007, held in Beijing, China in May 2007. The papers cover a large volume of topics in computational science and related areas, from multiscale physics to wireless networks, and from graph theory to tools for program development.
Approximate Computing Techniques
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Author : Alberto Bosio
language : en
Publisher: Springer Nature
Release Date : 2022-06-10
Approximate Computing Techniques written by Alberto Bosio and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-10 with Technology & Engineering categories.
This book serves as a single-source reference to the latest advances in Approximate Computing (AxC), a promising technique for increasing performance or reducing the cost and power consumption of a computing system. The authors discuss the different AxC design and validation techniques, and their integration. They also describe real AxC applications, spanning from mobile to high performance computing and also safety-critical applications.
Approximate Circuits
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Author : Sherief Reda
language : en
Publisher: Springer
Release Date : 2018-12-05
Approximate Circuits written by Sherief Reda and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-05 with Technology & Engineering categories.
This book provides readers with a comprehensive, state-of-the-art overview of approximate computing, enabling the design trade-off of accuracy for achieving better power/performance efficiencies, through the simplification of underlying computing resources. The authors describe in detail various efforts to generate approximate hardware systems, while still providing an overview of support techniques at other computing layers. The book is organized by techniques for various hardware components, from basic building blocks to general circuits and systems.
Automatic Control And Computer Sciences
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Author :
language : en
Publisher:
Release Date : 1990
Automatic Control And Computer Sciences written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Automatic control categories.
Approximate Computing
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Author : Weiqiang Liu
language : en
Publisher: Springer Nature
Release Date : 2022-08-22
Approximate Computing written by Weiqiang Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-22 with Technology & Engineering categories.
This book explores the technological developments at various levels of abstraction, of the new paradigm of approximate computing. The authors describe in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels. Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and applications developed in approximate computing for a wide scope of readership and specialists. Serves as a single-source reference to state-of-the-art of approximate computing; Covers broad range of topics, from circuits to applications; Includes contributions by leading researchers, from academia and industry.
Adaptive Health Management Information Systems Concepts Cases And Practical Applications
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Author : Joseph Tan
language : en
Publisher: Jones & Bartlett Learning
Release Date : 2019-09-17
Adaptive Health Management Information Systems Concepts Cases And Practical Applications written by Joseph Tan and has been published by Jones & Bartlett Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-17 with Medical categories.
Adaptive Health Management Information Systems, Fourth Edition is a thorough resource for a broad range of healthcare professionals–from informaticians, physicians and nurses, to pharmacists, public health and allied health professionals–who need to keep pace the digital transformation of health care. Wholly revised, updated, and expanded in scope, the fourth edition covers the latest developments in the field of health management information systems (HMIS) including big data analytics and machine learning in health care; precision medicine; digital health commercialization; supply chain management; informatics for pharmacy and public health; digital health leadership; cybersecurity; and social media analytics.
Advanced Intelligent Computing Theories And Applications With Aspects Of Theoretical And Methodological Issues
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Author : De-Shuang Huang
language : en
Publisher: Springer
Release Date : 2008-09-08
Advanced Intelligent Computing Theories And Applications With Aspects Of Theoretical And Methodological Issues written by De-Shuang Huang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-08 with Computers categories.
The International Conference on Intelligent Computing (ICIC) was formed to p- vide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and ind- try to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. ICIC 2008, held in Shanghai, China, September 15–18, 2008, constituted the 4th International Conference on Intelligent Computing. It built upon the success of ICIC 2007, ICIC 2006 and ICIC 2005 held in Qingdao, Kunming and Hefei, China, 2007, 2006 and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Emerging Intelligent Computing Technology and Applications”. Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
