Data Orchestration In Deep Learning Accelerators

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Data Orchestration in Deep Learning Accelerators

Tushar Krishna,Hyoukjun Kwon,Angshuman Parashar,Michael Pellauer,Ananda Samajdar
Data Orchestration in Deep Learning Accelerators Book PDF

Author : Tushar Krishna,Hyoukjun Kwon,Angshuman Parashar,Michael Pellauer,Ananda Samajdar
Publisher : Springer Nature
Page : 158 pages
File Size : 45,5 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017674

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Data Orchestration in Deep Learning Accelerators by Tushar Krishna,Hyoukjun Kwon,Angshuman Parashar,Michael Pellauer,Ananda Samajdar Pdf

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

Andres Rodriguez
Deep Learning Systems Book PDF

Author : Andres Rodriguez
Publisher : Springer Nature
Page : 245 pages
File Size : 49,6 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017698

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Deep Learning Systems by Andres Rodriguez Pdf

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.

Data Accelerator for AI and Analytics

Simon Lorenz,Gero Schmidt,TJ Harris,Mike Knieriemen,Nils Haustein,Abhishek Dave,Venkateswara Puvvada,Christof Westhues,IBM Redbooks
Data Accelerator for AI and Analytics Book PDF

Author : Simon Lorenz,Gero Schmidt,TJ Harris,Mike Knieriemen,Nils Haustein,Abhishek Dave,Venkateswara Puvvada,Christof Westhues,IBM Redbooks
Publisher : IBM Redbooks
Page : 88 pages
File Size : 43,5 Mb
Release : 2021-01-20
Category : Computers
ISBN : 9780738459325

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Data Accelerator for AI and Analytics by Simon Lorenz,Gero Schmidt,TJ Harris,Mike Knieriemen,Nils Haustein,Abhishek Dave,Venkateswara Puvvada,Christof Westhues,IBM Redbooks Pdf

This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.

Compact and Fast Machine Learning Accelerator for IoT Devices

Hantao Huang,Hao Yu
Compact and Fast Machine Learning Accelerator for IoT Devices Book PDF

Author : Hantao Huang,Hao Yu
Publisher : Springer
Page : 149 pages
File Size : 47,8 Mb
Release : 2018-12-07
Category : Technology & Engineering
ISBN : 9789811333231

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Compact and Fast Machine Learning Accelerator for IoT Devices by Hantao Huang,Hao Yu Pdf

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

R. Sujatha,S. L. Aarthy,R. Vettriselvan
Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Book PDF

Author : R. Sujatha,S. L. Aarthy,R. Vettriselvan
Publisher : CRC Press
Page : 216 pages
File Size : 42,9 Mb
Release : 2021-09-22
Category : Technology & Engineering
ISBN : 9781000454536

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Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by R. Sujatha,S. L. Aarthy,R. Vettriselvan Pdf

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

Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman
Deep Learning in Data Analytics Book PDF

Author : Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman
Publisher : Springer Nature
Page : 271 pages
File Size : 42,5 Mb
Release : 2021-08-11
Category : Technology & Engineering
ISBN : 9783030758554

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Deep Learning in Data Analytics by Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman Pdf

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.

Efficient Processing of Deep Neural Networks

Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
Efficient Processing of Deep Neural Networks Book PDF

Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
Publisher : Springer Nature
Page : 254 pages
File Size : 43,8 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017667

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Efficient Processing of Deep Neural Networks by Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer Pdf

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.

Data Science on AWS

Chris Fregly,Antje Barth
Data Science on AWS Book PDF

Author : Chris Fregly,Antje Barth
Publisher : "O'Reilly Media, Inc."
Page : 524 pages
File Size : 54,5 Mb
Release : 2021-04-07
Category : Computers
ISBN : 9781492079361

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Data Science on AWS by Chris Fregly,Antje Barth Pdf

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Deep Learning for Numerical Applications with SAS

Henry Bequet
Deep Learning for Numerical Applications with SAS Book PDF

Author : Henry Bequet
Publisher : SAS Institute
Page : 234 pages
File Size : 49,9 Mb
Release : 2018-07-20
Category : Computers
ISBN : 9781635266771

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Deep Learning for Numerical Applications with SAS by Henry Bequet Pdf

Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces—from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks. Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs. This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to Use deep learning in SAS Speed up their analytics using deep learning Easily write highly parallel programs using the many task computing paradigms This book is part of the SAS Press program.

TinyML

Pete Warden,Daniel Situnayake
TinyML Book PDF

Author : Pete Warden,Daniel Situnayake
Publisher : O'Reilly Media
Page : 504 pages
File Size : 54,6 Mb
Release : 2019-12-16
Category : Computers
ISBN : 9781492052012

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TinyML by Pete Warden,Daniel Situnayake Pdf

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Deep Learning

Li Deng,Dong Yu
Deep Learning Book PDF

Author : Li Deng,Dong Yu
Publisher : Unknown
Page : 212 pages
File Size : 40,6 Mb
Release : 2014
Category : Machine learning
ISBN : 1601988141

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Deep Learning by Li Deng,Dong Yu Pdf

Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks

A Clinical Guide to the Treatment of the Human Stress Response

George S. Everly Jr.
A Clinical Guide to the Treatment of the Human Stress Response Book PDF

Author : George S. Everly Jr.
Publisher : Springer Science & Business Media
Page : 390 pages
File Size : 43,8 Mb
Release : 2012-12-06
Category : Psychology
ISBN : 9781461307419

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A Clinical Guide to the Treatment of the Human Stress Response by George S. Everly Jr. Pdf

In 1981, Plenum Press published a text entitled The Nature and Treatment of the Stress Response by Robert Rosenfeld, M. D. , and me. That text attempted to do what no other text from a major publisher had previously attempted, that is, to create a clinically practical guide for the treatment of excessive stress and its arousal-related syndromes-this to be captured between the same covers in combination with a detailed, clinically relevant pedagogy on the neurological and endocrinological foundations of the stress re sponse itself. That volume has enjoyed considerable success having found markets among practicing professionals and clinical students as well. The fields of psychosomatic medicine, health psychology, behavioral medicine, and applied stress research have appreciably expanded their boundaries since the publication of the aforementioned volume. Although remarkably little of the clinical utility of that volume has been eroded with time, it was felt that an updated and more integrative clinical textbook needed to be offered to practicing clinicians and students within clinical rather than simply create a second edition of training programs. Therefore, was made to create a significantly revised the original volume, the decision and expanded volume that would cover many of the same topics as the original volume but would provide a primary emphasis on the treatment of excessive stress and that would employ an integrative phenomenological model to facilitate that end. This present volume entitled A Clinical Guide to the Treatment of the Human Stress Response is the result.

Conformal Prediction for Reliable Machine Learning

Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Conformal Prediction for Reliable Machine Learning Book PDF

Author : Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publisher : Newnes
Page : 323 pages
File Size : 45,8 Mb
Release : 2014-04-23
Category : Computers
ISBN : 9780124017153

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Conformal Prediction for Reliable Machine Learning by Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk Pdf

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Arun Kumar Sangaiah
Deep Learning and Parallel Computing Environment for Bioengineering Systems Book PDF

Author : Arun Kumar Sangaiah
Publisher : Academic Press
Page : 280 pages
File Size : 44,6 Mb
Release : 2019-07-26
Category : Computers
ISBN : 9780128172933

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Deep Learning and Parallel Computing Environment for Bioengineering Systems by Arun Kumar Sangaiah Pdf

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Deep Learning with Hadoop

Dipayan Dev
Deep Learning with Hadoop Book PDF

Author : Dipayan Dev
Publisher : Packt Publishing Ltd
Page : 200 pages
File Size : 55,9 Mb
Release : 2017-02-20
Category : Computers
ISBN : 9781787121232

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Deep Learning with Hadoop by Dipayan Dev Pdf

Build, implement and scale distributed deep learning models for large-scale datasets About This Book Get to grips with the deep learning concepts and set up Hadoop to put them to use Implement and parallelize deep learning models on Hadoop's YARN framework A comprehensive tutorial to distributed deep learning with Hadoop Who This Book Is For If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book. What You Will Learn Explore Deep Learning and various models associated with it Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it Implement Convolutional Neural Network (CNN) with deeplearning4j Delve into the implementation of Restricted Boltzmann Machines (RBM) Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN) Get hands on practice of deep learning and their implementation with Hadoop. In Detail This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop. Style and approach This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.