Approximate Computing Techniques

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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 Computing And Its Impact On Accuracy Reliability And Fault Tolerance

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Author : Gennaro S. Rodrigues
language : en
Publisher: Springer Nature
Release Date : 2022-11-16

Approximate Computing And Its Impact On Accuracy Reliability And Fault Tolerance written by Gennaro S. Rodrigues and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-16 with Technology & Engineering categories.

This book introduces the concept of approximate computing for software and hardware designs and its impact on the reliability of embedded systems. It presents approximate computing methods and proposes approximate fault tolerance techniques applied to programmable hardware and embedded software to provide reliability at low computational costs. The book also presents fault tolerance techniques based on approximate computing, thus presenting how approximate computing can be applied to safety-critical systems.

Energy Efficient And Runtime Based Approximate Computing Techniques For Image Processing Applications

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Author : Junqi Huang
language : en
Publisher:
Release Date : 2021

Energy Efficient And Runtime Based Approximate Computing Techniques For Image Processing Applications written by Junqi Huang 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.

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.

A Survey Of Techniques For Approximate Computing

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Author :
language : en
Publisher:
Release Date : 2016

A Survey Of Techniques For Approximate Computing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Approximate computing trades off computation quality with the effort expended and as rising performance demands confront with plateauing resource budgets, approximate computing has become, not merely attractive, but even imperative. Here, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU and FPGA), processor components, memory technologies etc., and programming frameworks for AC. Moreover, we classify these techniques based on several key characteristics to emphasize their similarities and differences. Finally, the aim of this paper is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.

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.

Design Automation Techniques For Approximation Circuits

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Author : Arun Chandrasekharan
language : en
Publisher: Springer
Release Date : 2018-10-10

Design Automation Techniques For Approximation Circuits written by Arun Chandrasekharan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-10 with Technology & Engineering categories.

This book describes reliable and efficient design automation techniques for the design and implementation of an approximate computing system. The authors address the important facets of approximate computing hardware design - from formal verification and error guarantees to synthesis and test of approximation systems. They provide algorithms and methodologies based on classical formal verification, synthesis and test techniques for an approximate computing IC design flow. This is one of the first books in Approximate Computing that addresses the design automation aspects, aiming for not only sketching the possibility, but providing a comprehensive overview of different tasks and especially how they can be implemented.

Learned Approximate Computing For Machine Learning

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Author : Tianmu Li
language : en
Publisher:
Release Date : 2023

Learned Approximate Computing For Machine Learning written by Tianmu Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

{Machine learning using deep neural networks is growing in popularity and is demanding increasing computation requirements at the same time. Approximate computing is a promising approach that trades accuracy for performance, and stochastic computing is an especially interesting approach that preserves the compute units of single-bit computation while allowing adjustable compute precision. This dissertation centers around enabling and improving stochastic computing for neural networks, while also discussing works that lead up to stochastic computing and how the techniques developed for stochastic computing are applied to other approximate computing methods and applications other than deep neural networks. We start with 3pxnet, which combines extreme quantization with model pruning. While 3pxnet achieves extremely compact models, it demonstrates limits of binarization, including the inability to scale to higher precision levels and performance bottlenecks from accumulation. This leads us to stochastic computing, which performs single-gate multiplications and additions on probabilistic bit streams. The initial SC neural network implementation in ACOUSTIC aims at maximizing SC performance benefits while achieving usable accuracy. This is achieved through design choices in stream representation, performance optimizations using pooling layers, and training modifications to make single-gate accumulation possible. The subsequent work in GEO improves the stream generation and computation aspects of stochastic computing and reduces the accuracy gap between stochastic computing and fixed-point computing. The accumulation part of SC is further optimized in REX-SC, which allows efficient modeling of SC accumulation during training. During these iterations of the SC algorithm, we developed efficient training pipelines that target various aspects of training for approximate computing. Both forward and backward passes of training are optimized, which allows us to demonstrate model convergence results using SC and other approximate computing methods with limited hardware resources. Finally, we apply the training concept to other applications. In LAC, we show that an almost arbitrary parameterized application can be trained to perform well with approximate computing. At the same time, we can search for the optimal hardware configuration using NAS techniques.

Imprecise And Approximate Computation

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Author : Swaminathan Natarajan
language : en
Publisher: Springer
Release Date : 2007-08-26

Imprecise And Approximate Computation written by Swaminathan Natarajan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-26 with Computers categories.

Real-time systems are now used in a wide variety of applications. Conventionally, they were configured at design to perform a given set of tasks and could not readily adapt to dynamic situations. The concept of imprecise and approximate computation has emerged as a promising approach to providing scheduling flexibility and enhanced dependability in dynamic real-time systems. The concept can be utilized in a wide variety of applications, including signal processing, machine vision, databases, networking, etc. For those who wish to build dynamic real-time systems which must deal safely with resource unavailability while continuing to operate, leading to situations where computations may not be carried through to completion, the techniques of imprecise and approximate computation facilitate the generation of partial results that may enable the system to operate safely and avert catastrophe. Audience: Of special interest to researchers. May be used as a supplementary text in courses on real-time systems.

Systematic Design Of Low Power Processing Elements Using Stochastic And Approximate Computing Techniques

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Author : Ardalan Najafi
language : en
Publisher:
Release Date : 2021

Systematic Design Of Low Power Processing Elements Using Stochastic And Approximate Computing Techniques written by Ardalan Najafi 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 approximate and stochastic computing have been developed, on the one hand, to address the diminishing gains of technology scaling, and on the other hand, to exploit the intrinsic error resilience of many applications. They, indeed, take advantage of the disparity between the level of accuracy required by the application and that provided by the computing system, for achieving energy efficiency. As of the most important constitutes of an integrated circuit, arithmetic units often lie within the critical path of a processing system. They play a vital role in determining the performance and power consumption of the computing system. In the past decade, the design of the approximate arithmetic units has been in the center of attentions of the VLSI design research community; resulting in a numerous proposed approximate designs in the literature. In spite of a decade work on the approximate computing, there are still unresolved challenges faced by digital designers. The concept of acceptable quality of the results forms the foundation of the approximate and stochastic computing. In view of this fact, it is crucially decisive to have a clear, quantifiable definition of what signifies an acceptable quality. Indeed, the current metrics most often do not capture the requirements of a target application, and hence, mislead to sub-optimal design options for the application. Moreover, non-systematic designs, lack of fair comparisons and reproducible research have resulted in somewhat limited progresses in the field of approximate and stochastic computing. Besides, the accuracy requirements of an application is not a static property and may change across the different phases of the application. Therefore, it is important to systematically develop approximate and stochastic computing platforms which offer a variety of output qualities. In this dissertation, the aim is to take fundamental steps towards resolving the aforementioned challenges. Correspondingly, the following contributions are made in this dissertation. First, to palliate the lack of expressiveness of current metrics, a new parameterizable metric which correlates more precisely to the accuracy of the applications is proposed in this dissertation. Afterwards, the importance of fair comparisons for approximate computing units is underlined in this work. Subsequently, through generalizing and systematically optimizing an architectural template for approximate adders, an architecture is proposed which outperforms its existing counterparts. A conceptual framework for the systematic design of approximate adders including hybrid and non-equally segmented approaches is developed next. The framework discriminates the scenarios where approximate processing does not provide significant benefits from those where it does; in this latter case, it aids in obtaining optimal configurations for the adders. Furthermore, in order to address the dynamic configuration of the error characteristics, a stochastically-tunable adder is proposed which reduces the energy-delay product considerably in comparison with its conventional counterpart. In addition, we develop data-dependent corrections for truncated multipliers, where the proposed architectures surpass the existing approximate multipliers in the literature. The applicability of the proposed methods, and in general approximate computing units is eventually studied in modern applications. The correlation between the errors of a single unit and the whole system's accuracy is also investigated in the applications.