Efficient Processing of Deep Neural Networks, Sze, Vivienne Chen, Yu-Hsin Yang, Tien-Ju Emer, Joel S.
Автор: Sze Vivienne, Chen Yu-Hsin, Yang Tien-Ju Название: Efficient Processing of Deep Neural Networks ISBN: 1681738317 ISBN-13(EAN): 9781681738314 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 85010.00 T Наличие на складе: Нет в наличии. Описание: A structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks, with techniques that don`t sacrifice accuracy or increase hardware costs.
Автор: Sze Vivienne, Chen Yu-Hsin, Yang Tien-Ju Название: Efficient Processing of Deep Neural Networks ISBN: 1681738333 ISBN-13(EAN): 9781681738338 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 104410.00 T Наличие на складе: Невозможна поставка. Описание: A structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks, with techniques that don`t sacrifice accuracy or increase hardware costs.
Автор: Vibhu Sharma; Francky Catthoor; Wim Dehaene Название: SRAM Design for Wireless Sensor Networks ISBN: 1489992154 ISBN-13(EAN): 9781489992154 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This reviews the most efficient circuit design techniques and trade-offs, and introduces new memory architecture techniques, sense amplifier circuits and voltage optimization methods for reducing the impact of variability in wireless sensor applications.
Автор: Marijn van Dongen; Wouter Serdijn Название: Design of Efficient and Safe Neural Stimulators ISBN: 3319281291 ISBN-13(EAN): 9783319281292 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Introduction.- Modeling the activation of Neural cells.- Electrode-tissue interface during a stimulation cycle.- Efficacy of high frequency switched-mode neural stimulation.- System design of neural stimulators.- Design of an arbitrary waveform charge balanced stimulator.- Switched-mode High Frequency Stimulator Design.- Conclusions.
Introduction.- Modeling the activation of Neural cells.- Electrode-tissue interface during a stimulation cycle.- Efficacy of high frequency switched-mode neural stimulation.- System design of neural stimulators.- Design of an arbitrary waveform charge balanced stimulator.- Switched-mode High Frequency Stimulator Design.- Conclusions.
Автор: Aggarwal Charu C. Название: Neural Networks and Deep Learning: A Textbook ISBN: 3030068560 ISBN-13(EAN): 9783030068561 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Нет в наличии. Описание: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Автор: Moons, Bert Bankman, Daniel Verhelst, Marian Название: Embedded deep learning ISBN: 3319992228 ISBN-13(EAN): 9783319992228 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
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.
Автор: Bert Moons; Daniel Bankman; Marian Verhelst Название: Embedded Deep Learning ISBN: 303007577X ISBN-13(EAN): 9783030075774 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Nele Reynders; Wim Dehaene Название: Ultra-Low-Voltage Design of Energy-Efficient Digital Circuits ISBN: 3319161350 ISBN-13(EAN): 9783319161358 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book focuses on increasing the energy-efficiency of electronic devices so that portable applications can have a longer stand-alone time on the same battery. The authors explain the energy-efficiency benefits that ultra-low-voltage circuits provide and provide answers to tackle the challenges which ultra-low-voltage operation poses.
Автор: Yu Lin; Hans Hegt; Kostas Doris; Arthur H.M. van R Название: Power-Efficient High-Speed Parallel-Sampling ADCs for Broadband Multi-carrier Systems ISBN: 331917679X ISBN-13(EAN): 9783319176796 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the challenges of designing high performance analog-to-digital converters (ADCs) based on the "smart data converters" concept, which implies context awareness, on-chip intelligence and adaptation.
Автор: Muhammad Usman Karim Khan; Muhammad Shafique; J?rg Название: Energy Efficient Embedded Video Processing Systems ISBN: 3319614541 ISBN-13(EAN): 9783319614540 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Providing the means to implement energy-efficient video systems by using different optimization approaches at multiple abstraction levels, this book evaluates the complete video system to optimize its components in synergy, increase throughput-per-watt, and address reliability issues, and providing algorithmic and architectural enhancements.
Автор: Kamran Souri; Kofi A.A. Makinwa Название: Energy-Efficient Smart Temperature Sensors in CMOS Technology ISBN: 3319623060 ISBN-13(EAN): 9783319623061 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book describes the design and implementation of energy-efficient smart (digital output) temperature sensors in CMOS technology. To accomplish this, a new readout topology, namely the zoom-ADC, is presented. It combines a coarse SAR-ADC with a fine Sigma-Delta (SD) ADC. The digital result obtained from the coarse ADC is used to set the reference levels of the SD-ADC, thereby zooming its full-scale range into a small region around the input signal. This technique considerably reduces the SD-ADC’s full-scale range, and notably relaxes the number of clock cycles needed for a given resolution, as well as the DC-gain and swing of the loop-filter. Both conversion time and power-efficiency can be improved, which results in a substantial improvement in energy-efficiency. Two BJT-based sensor prototypes based on 1st-order and 2nd-order zoom-ADCs are presented. They both achieve inaccuracies of less than ±0.2°C over the military temperature range (-55°C to 125°C). A prototype capable of sensing temperatures up to 200°C is also presented. As an alternative to BJTs, sensors based on dynamic threshold MOSTs (DTMOSTs) are also presented. It is shown that DTMOSTs are capable of achieving low inaccuracy (±0.4°C over the military temperature range) as well as sub-1V operation, making them well suited for use in modern CMOS processes.
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