An Opencl Framework For Real Time Inference Of Next Generation Convolutional Neural Networks On Fpgas

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An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs

Author : Sachin Kumawat
Publisher :
ISBN 13 : 9780355764413
Total Pages : pages
Book Rating : 4.7/5 (644 download)

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Book Synopsis An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs by : Sachin Kumawat

Download or read book An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs written by Sachin Kumawat and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference. In this work, we present FICaffe, a framework for FPGA-based Inference with Caffe, which provides a complete automated generation and mapping of CNN accelerators on FPGAs. We target applications with critical latency requirements and design high processing efficiency accelerators for CNNs. The architecture is structured in a highly concurrent OpenCL library, which enables High Level Synthesis tools to effectively exploit data, task and pipeline parallelism. We propose a unified memory model, that drives exploration of optimal design by matching on-chip and off-chip memory bandwidths available on FPGA platforms. We also identify origins of all clock cycle stalls and overheads inherent to CNN acceleration designs and provide a detailed model to accurately predict the runtime latency with less than 4% error against on-board tests. Furthermore, with FICaffe we provide support for cross-network synthesis, such that it is possible to processes a variety of CNNs, with reasonable efficiency, without long re-compilation hours. FICaffe is integrated with the popular deep learning framework Caffe, and is deployable to a wide variety of CNNs. FICaffe's efficacy is shown by mapping to a 28nm Stratix V GXA7 chip, and both network specific and cross-network performance are reported for AlexNet, VGG, SqueezeNet and GoogLeNet. We show a processing efficiency of 95.8% for the widely-reported VGG benchmark, which outperforms prior work. FICaffe also achieves more than 2X speedup on Stratix V GXA7 compared with the best published results on this chip, to the best of our knowledge.

Hardware Accelerators in Data Centers

Author : Christoforos Kachris
Publisher : Springer
ISBN 13 : 3319927922
Total Pages : 280 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Hardware Accelerators in Data Centers by : Christoforos Kachris

Download or read book Hardware Accelerators in Data Centers written by Christoforos Kachris and published by Springer. This book was released on 2018-08-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.

Framework for Mapping Convolutional Neural Networks on FPGAs

Author : Masoud Shahshahani
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Framework for Mapping Convolutional Neural Networks on FPGAs by : Masoud Shahshahani

Download or read book Framework for Mapping Convolutional Neural Networks on FPGAs written by Masoud Shahshahani and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) applications are on the rise. Recent advances in machine learning and deep learning have created various applications for medicine/healthcare, financial markets, security, entertainment, and social sciences. Deep Learning, especially, has demonstrated tremendous opportunities in computer vision, autonomous driving, natural language processing, and many more. Deep learning allows machines to solve complex problems using Artificial Neural Networks (ANNs), and the learning itself can be supervised or semisupervised. Multilayered artificial neural networks are called Deep Neural Networks (DNNs). These deep computational models are composed of multiple sequentially processing layers that help learn the representations within a given data set. Convolutional Neural Networks (CNN) are a particular class of deep networks that use convolution to extract features from (usually a time-domain or frequency-domain) data and then use the extracted features to classify that data for final inferencing. Several software tools and frameworks are available to facilitate the deep learning community with the fast development and high-performance execution of DNNs. Tool flows, such as PyTorch, Caffe, Theano, and TensorFlow, aim to increase the productivity of CNN software developers by providing a pathway for implementing deep networks on high-performance multi-core CPUs, GPUs, and DSPs. GPUs, especially, provide easy access to floating point operations and also allow very high memory bandwidths. Some of the latest Nvidia GPUs (Nvidia GeForce RTX2080) consume as much as 300 watts of power. Excessive power dissipation can make GPUs an unfavorable candidate for implementing CNNs for a variety of applications. Field Programmable Gate Arrays (FPGAs) provide a high degree of customized parallelization and offer far superior performance per watt. We believe that FPGA-based accelerators are ideal platforms for implementing Convolutional Neural Networks for computer vision and related applications. Software engineers with minimal hardware design skills demand tremendous support within the tool-flows, and FPGA vendors are fully embracing new methodologies like high-level synthesis, where the designs can be described as a program written in languages like C/C++. However, commercial FPGAs are resource-scarce, the CNN mapping design space is enormous, and efficient mapping of CNN can quickly become a challenging task. The requirement of FPGA resources, latency, and power is affected by many parameters, including the CNN architecture and the level of computational parallelism. In practice, a software designer first explores various CNN architectures in software to improve architecture0́9s validation accuracy. Once an architecture has been finalized, the designer ports the architecture design to FPGA for inference acceleration. The mapping process undergoes performance optimization by tweaking many design-related parameters during the design space exploration and changing the operating frequencies. The entire process is highly time-consuming. This dissertation describes a fully automated end-to-end design framework for implementing CNNs on FPGAs. The framework allows a designer to express the CNNs in commonly preferred Python language descriptions and provides a guided tool flow to generate a custom Intellectual Property (IP) block. In addition, the framework allows easy and complete exploration for selecting final design implementations based on optimization parameters that include Performance, Power, and Area (PPA).

Hardware Acceleration of Video Analytics on FPGA Using OpenCL

Author : Akshay Dua
Publisher :
ISBN 13 :
Total Pages : 44 pages
Book Rating : 4.:/5 (131 download)

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Book Synopsis Hardware Acceleration of Video Analytics on FPGA Using OpenCL by : Akshay Dua

Download or read book Hardware Acceleration of Video Analytics on FPGA Using OpenCL written by Akshay Dua and published by . This book was released on 2019 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the exponential growth in video content over the period of the last few years, analysis of videos is becoming more crucial for many applications such as self-driving cars, healthcare, and traffic management. Most of these video analysis application uses deep learning algorithms such as convolution neural networks (CNN) because of their high accuracy in object detection. Thus enhancing the performance of CNN models become crucial for video analysis. CNN models are computationally-expensive operations and often require high-end graphics processing units (GPUs) for acceleration. However, for real-time applications in an energy-thermal constrained environment such as traffic management, GPUs are less preferred because of their high power consumption, limited energy efficiency. They are challenging to fit in a small place. To enable real-time video analytics in emerging large scale Internet of things (IoT) applications, the computation must happen at the network edge (near the cameras) in a distributed fashion. Thus, edge computing must be adopted. Recent studies have shown that field-programmable gate arrays (FPGAs) are highly suitable for edge computing due to their architecture adaptiveness, high computational throughput for streaming processing, and high energy efficiency. This thesis presents a generic OpenCL-defined CNN accelerator architecture optimized for FPGA-based real-time video analytics on edge. The proposed CNN OpenCL kernel adopts a highly pipelined and parallelized 1-D systolic array architecture, which explores both spatial and temporal parallelism for energy efficiency CNN acceleration on FPGAs. The large fan-in and fan-out of computational units to the memory interface are identified as the limiting factor in existing designs that causes scalability issues, and solutions are proposed to resolve the issue with compiler automation. The proposed CNN kernel is highly scalable and parameterized by three architecture parameters, namely pe_num, reuse_fac, and vec_fac, which can be adapted to achieve 100% utilization of the coarse-grained computation resources (e.g., DSP blocks) for a given FPGA. The proposed CNN kernel is generic and can be used to accelerate a wide range of CNN models without recompiling the FPGA kernel hardware. The performance of Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet has been measured by the proposed CNN kernel on Intel Arria 10 GX1150 FPGA. The measurement result shows that the proposed CNN kernel, when mapped with 100% utilization of computation resources, can achieve a latency of 11ms, 84ms, 1614.9ms, and 990.34ms for Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet respectively when the input feature maps and weights are represented using 32-bit floating-point data type.

Caffeinated FPGAs

Author : Roberto DiCecco
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Caffeinated FPGAs by : Roberto DiCecco

Download or read book Caffeinated FPGAs written by Roberto DiCecco and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a framework for performing training and inference of Convolutional Neural Networks (CNNs) with reduced precision floating-point arithmetic. This work aims to provide a means for FPGA and machine learning researchers to use the customizability of FPGAs to explore the precision requirements of training CNNs with an open-source framework. This is accomplished through the creation of a High-Level Synthesis library with a Custom Precision Floating-Point data type that is configurable in both exponent and mantissa widths, with several standard operators and rounding modes supported. With this library a FPGA CNN Training Engine (FCTE) has been created along with a FPGA CNN framework FPGA Caffe, which is built on Caffe. FCTE has a peak performance of approximately 350 GFLOPs, and has been used to show that a mantissa width of 5 and exponent width of 6 is sufficient for training several models targeting the MNIST and CIFAR-10 datasets.

Automated Customization of ML Inference on FPGAs

Author : Mohammad Ghasemzadeh
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Automated Customization of ML Inference on FPGAs by : Mohammad Ghasemzadeh

Download or read book Automated Customization of ML Inference on FPGAs written by Mohammad Ghasemzadeh and published by . This book was released on 2018 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces novel frameworks for automated customization of two classes of machine learning algorithms, deep neural networks and causal Bayesian analysis. The high computational complexity often prohibits the deployment of ML models on resource-constrained embedded devices where memory and energy budgets are strictly limited. FPGAs offer a flexible substrate that can be configured to maximally exploit the parallel nature of computations in different ML algorithms to deliver high-throughput and power-efficient accelerators. To make FPGAs a ubiquitous platform for ML inference, automated frameworks that can customize ML models to the constraints of the underlying hardware and pertinent application requirements are necessary. My work proposes hardware-algorithm co-design approaches to customize ML inference on FPGA platforms and provides end-to-end automated frameworks to generate optimized hardware accelerators which can be used by a broad range of ML developers without requiring any hardware design knowledge. My key contributions include: (i) proposing an end-to-end framework to customize execution of deep neural networks on FPGAs using a reconfigurable encoding approach for the parameters of model which results in 9-fold reduction in memory footprint and 15-fold improvement in throughput without any loss in accuracy, (ii) proposing CausaLearn, the first automated framework that enables real-time and scalable approximation of probability density function in the context of causal Bayesian analysis which offers up to two orders-of-magnitude runtime and energy improvements compared to the best-known prior solution, (iii) proposing ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA.

Vision on the Edge

Author : Wei-Yin Ko
Publisher :
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Vision on the Edge by : Wei-Yin Ko

Download or read book Vision on the Edge written by Wei-Yin Ko and published by . This book was released on 2018 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

Author : Jonathan Greene
Publisher :
ISBN 13 : 9781450343541
Total Pages : pages
Book Rating : 4.3/5 (435 download)

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Book Synopsis Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays by : Jonathan Greene

Download or read book Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays written by Jonathan Greene and published by . This book was released on 2017-02-22 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: FPGA '17: The 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Feb 22, 2017-Feb 24, 2017 Monterey, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

High-Performance Computing Using FPGAs

Author : Wim Vanderbauwhede
Publisher : Springer Science & Business Media
ISBN 13 : 1461417910
Total Pages : 798 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis High-Performance Computing Using FPGAs by : Wim Vanderbauwhede

Download or read book High-Performance Computing Using FPGAs written by Wim Vanderbauwhede and published by Springer Science & Business Media. This book was released on 2013-08-23 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Performance Computing using FPGA covers the area of high performance reconfigurable computing (HPRC). This book provides an overview of architectures, tools and applications for High-Performance Reconfigurable Computing (HPRC). FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity and capabilities of FPGAs, and the advent of multicore processors which has caused the acceptance of parallel computational models. The Part on architectures will introduce different FPGA-based HPC platforms: attached co-processor HPRC architectures such as the CHREC’s Novo-G and EPCC’s Maxwell systems; tightly coupled HRPC architectures, e.g. the Convey hybrid-core computer; reconfigurably networked HPRC architectures, e.g. the QPACE system, and standalone HPRC architectures such as EPFL’s CONFETTI system. The Part on Tools will focus on high-level programming approaches for HPRC, with chapters on C-to-Gate tools (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical tools (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for example OpenCL, Microsoft’s Kiwi and Alchemy projects). The part on Applications will present case from several application domains where HPRC has been used successfully, such as Bioinformatics and Computational Biology; Financial Computing; Stencil computations; Information retrieval; Lattice QCD; Astrophysics simulations; Weather and climate modeling.

Robotic Computing on FPGAs

Author : Shaoshan Liu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1636391664
Total Pages : 220 pages
Book Rating : 4.6/5 (363 download)

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Book Synopsis Robotic Computing on FPGAs by : Shaoshan Liu

Download or read book Robotic Computing on FPGAs written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2021-06-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.

Reconfigurable Computing

Author : Scott Hauck
Publisher : Elsevier
ISBN 13 : 0080556019
Total Pages : 945 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Reconfigurable Computing by : Scott Hauck

Download or read book Reconfigurable Computing written by Scott Hauck and published by Elsevier. This book was released on 2010-07-26 with total page 945 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reconfigurable Computing marks a revolutionary and hot topic that bridges the gap between the separate worlds of hardware and software design— the key feature of reconfigurable computing is its groundbreaking ability to perform computations in hardware to increase performance while retaining the flexibility of a software solution. Reconfigurable computers serve as affordable, fast, and accurate tools for developing designs ranging from single chip architectures to multi-chip and embedded systems. Scott Hauck and Andre DeHon have assembled a group of the key experts in the fields of both hardware and software computing to provide an introduction to the entire range of issues relating to reconfigurable computing. FPGAs (field programmable gate arrays) act as the “computing vehicles to implement this powerful technology. Readers will be guided into adopting a completely new way of handling existing design concerns and be able to make use of the vast opportunities possible with reconfigurable logic in this rapidly evolving field. Designed for both hardware and software programmers Views of reconfigurable programming beyond standard programming languages Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Deep Learning for Computer Architects

Author : Brandon Reagen
Publisher : Springer Nature
ISBN 13 : 3031017560
Total Pages : 109 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning for Computer Architects by : Brandon Reagen

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail 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, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Deep Learning with Azure

Author : Mathew Salvaris
Publisher : Apress
ISBN 13 : 1484236793
Total Pages : 298 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Deep Learning with Azure by : Mathew Salvaris

Download or read book Deep Learning with Azure written by Mathew Salvaris and published by Apress. This book was released on 2018-08-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Deep Learning in Computer Vision

Author : Mahmoud Hassaballah
Publisher : CRC Press
ISBN 13 : 1351003801
Total Pages : 261 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Deep Learning in Computer Vision by : Mahmoud Hassaballah

Download or read book Deep Learning in Computer Vision written by Mahmoud Hassaballah and published by CRC Press. This book was released on 2020-03-23 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Synthesis Techniques and Optimizations for Reconfigurable Systems

Author : Ryan Kastner
Publisher : Springer Science & Business Media
ISBN 13 : 9781402076985
Total Pages : 264 pages
Book Rating : 4.0/5 (769 download)

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Book Synopsis Synthesis Techniques and Optimizations for Reconfigurable Systems by : Ryan Kastner

Download or read book Synthesis Techniques and Optimizations for Reconfigurable Systems written by Ryan Kastner and published by Springer Science & Business Media. This book was released on 2003-10-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Synthesis Techniques and Optimization for Reconfigurable Systems discusses methods used to model reconfigurable applications at the system level, many of which could be incorporated directly into modern compilers. The book also discusses a framework for reconfigurable system synthesis, which bridges the gap between application-level compiler analysis and high-level device synthesis. The development of this framework (discussed in Chapter 5), and the creation of application analysis which further optimize its output (discussed in Chapters 7, 8, and 9), represent over four years of rigorous investigation within UCLA's Embedded and Reconfigurable Laboratory (ERLab) and UCSB's Extensible, Programmable and Reconfigirable Embedded SystemS (ExPRESS) Group. The research of these systems has not yet matured, and we continually strive to develop data and methods, which will extend the collective understanding of reconfigurable system synthesis.

Creating Autonomous Vehicle Systems

Author : Shaoshan Liu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681731673
Total Pages : 285 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis Creating Autonomous Vehicle Systems by : Shaoshan Liu

Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Frontiers in Massive Data Analysis

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287812
Total Pages : 191 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Frontiers in Massive Data Analysis by : National Research Council

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.