Approximate Computing Techniques PDF Download
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Author: Alberto Bosio
Publisher: Springer Nature
ISBN: 303094705X
Category : Technology & Engineering
Languages : en
Pages : 541
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Book Description
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.
Author: Alberto Bosio
Publisher: Springer Nature
ISBN: 303094705X
Category : Technology & Engineering
Languages : en
Pages : 541
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Book Description
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.
Author: Sherief Reda
Publisher: Springer
ISBN: 9783319993218
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
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.
Author: Weiqiang Liu
Publisher: Springer Nature
ISBN: 3030983471
Category : Technology & Engineering
Languages : en
Pages : 607
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Book Description
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.
Author: Gennaro S. Rodrigues
Publisher: Springer Nature
ISBN: 3031157176
Category : Technology & Engineering
Languages : en
Pages : 137
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Book Description
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.
Author: Sherief Reda
Publisher: Springer
ISBN: 3319993224
Category : Technology & Engineering
Languages : en
Pages : 479
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Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 33
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Book Description
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.
Author: Kostas Siozios
Publisher: Springer
ISBN: 3030036405
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
This book explains the fundamentals of control theory for Internet of Things (IoT) systems and smart grids and its applications. It discusses the challenges imposed by large-scale systems, and describes the current and future trends and challenges in decision-making for IoT in detail, showing the ongoing industrial and academic research in the field of smart grid domain applications. It presents step-by-step design guidelines for the modeling, design, customisation and calibration of IoT systems applied to smart grids, in which the challenges increase with each system’s increasing complexity. It also provides solutions and detailed examples to demonstrate how to use the techniques to overcome these challenges, as well as other problems related to decision-making for successful implementation. Further, it anaylses the features of decision-making, such as low-complexity and fault-tolerance, and uses open-source and publicly available software tools to show readers how they can design, implement and customise their own system control instantiations. This book is a valuable resource for power engineers and researchers, as it addresses the analysis and design of flexible decision-making mechanisms for smart grids. It is also of interest to students on courses related to control of large-scale systems, since it covers the use of state-of-the-art technology with examples and solutions in every chapter. And last but not least, it offers practical advice for professionals working with smart grids.
Author: Tianmu Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
{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.
Author: Arun Chandrasekharan
Publisher: Springer
ISBN: 3319989650
Category : Technology & Engineering
Languages : en
Pages : 130
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Book Description
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.
Author: Bert Moons
Publisher: Springer
ISBN: 3319992236
Category : Technology & Engineering
Languages : en
Pages : 206
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Book Description
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
