Data Orchestration In Deep Learning Accelerators
DOWNLOAD
Download Data Orchestration In Deep Learning Accelerators PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Orchestration In Deep Learning Accelerators book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Data Orchestration In Deep Learning Accelerators
DOWNLOAD
Author : Tushar Krishna
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Data Orchestration In Deep Learning Accelerators written by Tushar Krishna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.
Deep Learning Systems
DOWNLOAD
Author : Andres Rodriguez
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning Systems written by Andres Rodriguez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Algorithm Accelerator Co Design For High Performance And Secure Deep Learning
DOWNLOAD
Author : Weizhe Hua
language : en
Publisher:
Release Date : 2022
Algorithm Accelerator Co Design For High Performance And Secure Deep Learning written by Weizhe Hua 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.
Deep learning has emerged as a new engine for many of today's artificial intelligence/machine learning systems, leading to several recent breakthroughs in vision and natural language processing tasks.However, as we move into the era of deep learning with billions and even trillions of parameters, meeting the computational and memory requirements to train and serve state-of-the-art models has become extremely challenging. Optimizing the computational cost and memory footprint of deep learning models for better system performance is critical to the widespread deployment of deep learning. Moreover, a massive amount of sensitive and private user data is exposed to the deep learning system during the training or serving process. Therefore, it is essential to investigate potential vulnerabilities in existing deep learning hardware, and then design secure deep learning systems that provide strong privacy guarantees for user data and the models that learn from the data. In this dissertation, we propose to co-design the deep learning algorithms and hardware architectural techniques to improve both the performance and security/privacy of deep learning systems. On high-performance deep learning, we first introduce channel gating neural network (CGNet), which exploits the dynamic sparsity of specific inputs to reduce computation of convolutional neural networks. We also co-develop an ASIC accelerator for CGNet that can turn theoretical FLOP reduction into wall-clock speedup. Secondly, we present Fast Linear Attention with a Single Head (FLASH), a state-of-the-art language model specifically designed for Google's TPU that can achieve transformer-level quality with linear complexity with respect to the sequence length. Through our empirical studies on masked language modeling, auto-regressive language modeling, and fine-tuning for question answering, FLASH achieves at least similar if not better quality compared to the augmented transformer, while being significantly faster (e.g., up to 12 times faster). On the security of deep learning, we study the side-channel vulnerabilities of existing deep learning accelerators. We then introduce a secure accelerator architecture for privacy-preserving deep learning, named GuardNN. GuardNN provides a trusted execution environment (TEE) with specialized protection for deep learning, and achieves a small trusted computing base and low protection overhead at the same time. The FPGA prototype of GuardNN achieves a maximum performance overhead of 2.4\% across four different modern DNNs models for ImageNet.
Exploiting Data Characteristics In The Design Of Accelerators For Deep Learning
DOWNLOAD
Author : Patrick H. Judd
language : en
Publisher:
Release Date : 2019
Exploiting Data Characteristics In The Design Of Accelerators For Deep Learning written by Patrick H. Judd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
The recent "Cambrian explosion" of Deep Learning (DL) algorithms in concert with the end of Moore's Law and Dennard Scaling has spurred interest in the design of custom hardware accelerators for DL algorithms. While DL has progressed quickly thanks in part to the abundance of efficient parallel computation provided by General Purpose Graphics Processing Units, newer DL algorithms demand even higher levels of compute density and efficiency. Furthermore, applications of DL in the mobile and embedded domains demand the energy efficiency of special purpose hardware. DL algorithms are dominated by large matrix-vector product computations, making them ideal targets for wide Single Instruction Multiple Data architectures. For the most part, efficiently mapping the structure of these computations to hardware is straightforward. Building on such designs, this thesis examines the data characteristics of these computations and proposes hardware modifications to exploit them for performance and energy efficiency. Specifically, this thesis examines the sparsity and precision requirements of Deep Convolutional Neural Networks, which comprise multiple layers of matrix-vector product computations. We propose a profiling method to find per layer reduced precision configurations while maintaining high classification accuracy. Following this, we propose three accelerator designs that build on top of the state-of-the-art DaDianNao accelerator. 1) Proteus exploits the reduced precision profiles by adding a light weight memory compression layer, saving energy in memory access and communication, and enabling larger networks in a fixed memory budget. 2) Cnvlutin exploits the presence of zero, and near zero, values in the inter-layer data by applying sparse compression to the data stream while maintain efficient utilization of the wide memory and compute structure of the SIMD accelerator. 3) Stripes exploits the reduced precision profiles for performance by processing data bit-serially, compensating for serial latency by exploiting the abundant parallelism in the convolution operation. All three designs exploit approximation, in terms of reduced precision and computation skipping to improve energy efficiency and/or performance while maintaining high classification accuracy. By approximating more aggressively, these designs can also dynamically trade-off accuracy for further improvements in performance and energy.
Deep Learning
DOWNLOAD
Author : Josh Patterson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-07-28
Deep Learning written by Josh Patterson and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Computers categories.
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Deep Learning For Data Architects
DOWNLOAD
Author : Shekhar Khandelwal
language : en
Publisher: BPB Publications
Release Date : 2023-08-16
Deep Learning For Data Architects written by Shekhar Khandelwal and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-16 with Computers categories.
A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ● Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ● Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. DESCRIPTION “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. WHAT YOU WILL LEARN ● Develop a comprehensive understanding of neural networks' key concepts and principles. ● Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ● Build and implement predictive models using various neural networks ● Learn how to use Transformers for complex NLP tasks ● Explore techniques to enhance the performance of your deep learning models. WHO THIS BOOK IS FOR This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. TABLE OF CONTENTS 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
Deep Learning For Computer Architects
DOWNLOAD
Author : Brandon Reagen
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-08-22
Deep Learning For Computer Architects written by Brandon Reagen and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-22 with Computers categories.
This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics
DOWNLOAD
Author : R. Sujatha
language : en
Publisher: CRC Press
Release Date : 2021-09-22
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics written by R. Sujatha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Technology & Engineering categories.
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.
Deep Learning In Data Analytics
DOWNLOAD
Author : Debi Prasanna Acharjya
language : en
Publisher: Springer Nature
Release Date : 2021-08-11
Deep Learning In Data Analytics written by Debi Prasanna Acharjya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-11 with Technology & Engineering categories.
This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Advanced Deep Learning Applications In Big Data Analytics
DOWNLOAD
Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2020-10-16
Advanced Deep Learning Applications In Big Data Analytics written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.