Efficient Processing of Deep Neural Networks PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Efficient Processing of Deep Neural Networks PDF full book. Access full book title Efficient Processing of Deep Neural Networks by Vivienne Sze. Download full books in PDF and EPUB format.
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Ravi Shanker Raju (Ph.D.) Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In recent years, deep neural networks have surpassed human performance on image classification tasks and and speech recognition. While current models can reach state of the art performance on stand-alone benchmarks, deploying them on embedded systems that have real-time latency deadlines either cause them to fail these requirements or severely get degraded in performance to meet the stated specifications. This requires intelligent design of the network architecture in order to minimize the accuracy degradation while deployed on the edge. Similarly, deep learning often has a long turn-around time due to the volume of the experiments on different hyperparameters and consumes time and resources. This motivates a need for developing training strategies that allow researchers who do not have access to large computational resources to train large models without waiting for exorbitant training cycles to be completed. This dissertation addresses these concerns through data dependent pruning of deep learning computation. First, regarding inference, we propose an integration of two different conditional execution strategies we call FBS-pruned CondConv by noticing that if we use input-specific filters instead of standard convolutional filters, we can aggressively prune at higher rates and mitigate accuracy degradation for significant computation savings. Then, regarding long training times, we introduce our dynamic data pruning framework which takes ideas from active learning and reinforcement learning to dynamically select subsets of data to train the model. Finally, as opposed to pruning data and in the same spirit of reducing training time, we investigate the vision transformer and introduce a unique training method called PatchDrop (originally designed for robustness to occlusions on transformers [1]), which uses the self-supervised DINO [2] model to identify the salient patches in an image and train on the salient subsets of an image. These strategies/training methods take a step in a direction to make models more accessible to deploy on edge devices in an efficient inference context and reduces the barrier for the independent researcher to train deep learning models which would require immense computational resources, pushing towards the democratization of machine learning.
Author: Ravi Shanker Raju (Ph.D.) Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In recent years, deep neural networks have surpassed human performance on image classification tasks and and speech recognition. While current models can reach state of the art performance on stand-alone benchmarks, deploying them on embedded systems that have real-time latency deadlines either cause them to fail these requirements or severely get degraded in performance to meet the stated specifications. This requires intelligent design of the network architecture in order to minimize the accuracy degradation while deployed on the edge. Similarly, deep learning often has a long turn-around time due to the volume of the experiments on different hyperparameters and consumes time and resources. This motivates a need for developing training strategies that allow researchers who do not have access to large computational resources to train large models without waiting for exorbitant training cycles to be completed. This dissertation addresses these concerns through data dependent pruning of deep learning computation. First, regarding inference, we propose an integration of two different conditional execution strategies we call FBS-pruned CondConv by noticing that if we use input-specific filters instead of standard convolutional filters, we can aggressively prune at higher rates and mitigate accuracy degradation for significant computation savings. Then, regarding long training times, we introduce our dynamic data pruning framework which takes ideas from active learning and reinforcement learning to dynamically select subsets of data to train the model. Finally, as opposed to pruning data and in the same spirit of reducing training time, we investigate the vision transformer and introduce a unique training method called PatchDrop (originally designed for robustness to occlusions on transformers [1]), which uses the self-supervised DINO [2] model to identify the salient patches in an image and train on the salient subsets of an image. These strategies/training methods take a step in a direction to make models more accessible to deploy on edge devices in an efficient inference context and reduces the barrier for the independent researcher to train deep learning models which would require immense computational resources, pushing towards the democratization of machine learning.
Author: Alexandros Iosifidis Publisher: Academic Press ISBN: 0323885721 Category : Technology & Engineering Languages : en Pages : 638
Book Description
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author: Osval Antonio Montesinos López Publisher: Springer Nature ISBN: 3030890104 Category : Technology & Engineering Languages : en Pages : 707
Book Description
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author: George K. Thiruvathukal Publisher: CRC Press ISBN: 1000540960 Category : Computers Languages : en Pages : 395
Book Description
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
Author: David Hanes Publisher: Cisco Press ISBN: 0134307089 Category : Computers Languages : en Pages : 782
Book Description
Today, billions of devices are Internet-connected, IoT standards and protocols are stabilizing, and technical professionals must increasingly solve real problems with IoT technologies. Now, five leading Cisco IoT experts present the first comprehensive, practical reference for making IoT work. IoT Fundamentals brings together knowledge previously available only in white papers, standards documents, and other hard-to-find sources—or nowhere at all. The authors begin with a high-level overview of IoT and introduce key concepts needed to successfully design IoT solutions. Next, they walk through each key technology, protocol, and technical building block that combine into complete IoT solutions. Building on these essentials, they present several detailed use cases, including manufacturing, energy, utilities, smart+connected cities, transportation, mining, and public safety. Whatever your role or existing infrastructure, you’ll gain deep insight what IoT applications can do, and what it takes to deliver them. Fully covers the principles and components of next-generation wireless networks built with Cisco IOT solutions such as IEEE 802.11 (Wi-Fi), IEEE 802.15.4-2015 (Mesh), and LoRaWAN Brings together real-world tips, insights, and best practices for designing and implementing next-generation wireless networks Presents start-to-finish configuration examples for common deployment scenarios Reflects the extensive first-hand experience of Cisco experts
Author: Kayo Matsushita Publisher: Springer ISBN: 9811056609 Category : Education Languages : en Pages : 228
Book Description
This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.
Author: Ian Goodfellow Publisher: MIT Press ISBN: 0262337371 Category : Computers Languages : en Pages : 801
Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: Vivienne Sze
Author: Vivienne Sze
Author: Ravi Shanker Raju (Ph.D.)
Author: Ravi Shanker Raju (Ph.D.)
Author: Alexandros Iosifidis
Author: Osval Antonio Montesinos López
Author: George K. Thiruvathukal
Author: David Hanes
Author: Daniel A. Roberts
Author: Kayo Matsushita
Author: Ian Goodfellow