Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA 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 Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA PDF full book. Access full book title Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA by Solomon Negussie Tesema. Download full books in PDF and EPUB format.
Author: Solomon Negussie Tesema Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Object detection is one of the most challenging yet essential computer vision research areas. It means labeling and localizing all known objects of interest on an input image using tightly fit rectangular bounding boxes around the objects. Object detection, having passed through several evolutions and progressions, nowadays relies on the successes of image classification networks based on deep convolutional neural networks. However, as the depth and complication of convolutional neural networks increased, detection speed reduced, and accuracy increased. Unfortunately, most computer vision applications, such as real-time object tracking on an embedded system, requires lightweight, fast and accurate object detection. As a result, object detection acceleration has become a hot research area, with much attention given to FPGA-based acceleration due to FPGA's high-energy efficiency, high-data bandwidth, and flexible programmability.This Ph.D. dissertation proposes incrementally improving object detection models by repurposing existing well-known object detectors into lighter, more accurate, and faster models. Our models achieve a comparable accuracy while being lightweight and faster compared with some of the top state-of-the-art detectors. We also propose and implement object detection inference acceleration using FPGA boards of different capacities and resources. We focus on high resource and energy-efficient inference acceleration implementations while preserving the object detector's accuracy performance. Last but not least, we present various auxiliary contributions such as a highly significant synthetic image generation or augmentation technique for training an object detector which is critical for achieving a high-performance object detector. Overall, our work in this thesis has two parts: designing and implementing lightweight and accurate CPU and GPU-based object detection models and implementing high-throughput, energy, and resource-efficient object detection inference acceleration on an FPGA.
Author: Solomon Negussie Tesema Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Object detection is one of the most challenging yet essential computer vision research areas. It means labeling and localizing all known objects of interest on an input image using tightly fit rectangular bounding boxes around the objects. Object detection, having passed through several evolutions and progressions, nowadays relies on the successes of image classification networks based on deep convolutional neural networks. However, as the depth and complication of convolutional neural networks increased, detection speed reduced, and accuracy increased. Unfortunately, most computer vision applications, such as real-time object tracking on an embedded system, requires lightweight, fast and accurate object detection. As a result, object detection acceleration has become a hot research area, with much attention given to FPGA-based acceleration due to FPGA's high-energy efficiency, high-data bandwidth, and flexible programmability.This Ph.D. dissertation proposes incrementally improving object detection models by repurposing existing well-known object detectors into lighter, more accurate, and faster models. Our models achieve a comparable accuracy while being lightweight and faster compared with some of the top state-of-the-art detectors. We also propose and implement object detection inference acceleration using FPGA boards of different capacities and resources. We focus on high resource and energy-efficient inference acceleration implementations while preserving the object detector's accuracy performance. Last but not least, we present various auxiliary contributions such as a highly significant synthetic image generation or augmentation technique for training an object detector which is critical for achieving a high-performance object detector. Overall, our work in this thesis has two parts: designing and implementing lightweight and accurate CPU and GPU-based object detection models and implementing high-throughput, energy, and resource-efficient object detection inference acceleration on an FPGA.
Author: Jonathan Greene Publisher: ISBN: 9781450343541 Category : Languages : en Pages :
Book Description
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.
Author: Mohammad Motamedi Publisher: ISBN: 9781085572187 Category : Languages : en Pages :
Book Description
Deep Convolutional Neural Networks (CNNs) exhibit remarkable performance in many pattern recognition, segmentation, classification, and comprehension tasks that were widely considered open problems for most of the computing history. For example, CNNs are shown to outperform humans in certain visual object recognition tasks. Given the significant potential of CNNs in advancing autonomy and intelligence in systems, the Internet of Things (IoT) research community has witnessed a surge in demand for CNN-enabled data processing, technically referred to as inference, for critical tasks, such as visual, voice and language comprehension. Inference using modern CNNs involves billions of operations on millions of parameters, and thus their deployment requires significant compute, storage, and energy resources. However, such resources are scarce in many resource-constrained IoT applications. Designing an efficient CNN architecture is the first step in alleviating this problem. Use of asymmetric kernels, breadth control techniques, and reduce-expand structures are among the most important approaches that can effectively decrease CNNs parameter budget and their computational intensity. The architectural efficiency can be further improved by eliminating ineffective neurons using pruning algorithms, and quantizing the parameters to decrease the model size. Hardware-driven optimization is the subsequent step in addressing the computational demands of deep neural networks. Mobile System on Chips (SoCs), which usually include a mobile GPU, a DSP, and a number of CPU cores, are great candidates for CNN inference on embedded platforms. Depending on the application, it is also possible to develop customized FPGA-based and ASIC-based accelerators. ASIC-based acceleration drastically outperforms other approaches in terms of both power consumption and execution time. However, using this approach is reasonable only if designing a new chip is economically justifiable for the target application. This dissertation aims to bridge the gap between computational demands of CNNs and computational capabilities of embedded platforms. We contend that one has to strike a judicious balance between functional requirements of a CNN, and its resource requirements, for an IoT application to be able to utilize the CNN. We investigate several concrete formulations of this broad concept, and propose effective approaches for addressing the identified challenges. First, we target platforms that are equipped with reconfigurable fabric, such as Field Programmable Gate Arrays (FPGA), and offer a framework for generation of optimized FPGA-based CNN accelerators. Our solution leverages an analytical approach to characterization and exploration of the accelerator design space through which, it synthesizes an efficient accelerator for a given CNN on a specific FPGA. Second, we investigate the problem of CNN inference on mobile SoCs, propose effective approaches for CNN parallelization targeting such platforms, and explore the underlying tradeoffs. Finally, in the last part of this dissertation, we investigate utilization of an existing optimized CNN model to automatically generate a competitive CNN for an IoT application whose objects of interest are a fraction of categories that the original CNN was designed to classify, such that the resource requirement of inference using the synthesized CNN is proportionally scaled down. We use the term resource scalability to refer to this concept and propose solutions for automated synthesis of context-aware, resource-scalable CNNs that meet the functional requirements of the target IoT application at fraction of the resource requirements of the original CNN.
Author: S Poonkuntran Publisher: CRC Press ISBN: 1000686795 Category : Computers Languages : en Pages : 345
Book Description
Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection
Author: Eko Cahyono Putro Publisher: Kreatif ISBN: 6237898735 Category : Computers Languages : id Pages : 215
Book Description
Dalam istilah praktis, deep learning merupakan bagian dari machine learning. Sebuah model machine learning perlu 'diberitahu' untuk bagaimana ia menciptakan prediksi akurat, dengan terus diberikan data. Sementara model deep learning dapat mempelajari metode komputasinya sendiri, dengan 'otaknya' sendiri, apabila diibaratkan. Sebuah model deep learning dirancang untuk terus menganalisis data dengan struktur logika yang mirip dengan bagaimana manusia mengambil keputusan. Untuk dapat mencapai kemampuan itu, deep learning menggunakan struktur algoritma berlapis yang disebut artificial neural network (ANN).
Author: Umberto Michelucci Publisher: ISBN: 9781484249772 Category : Machine learning Languages : en Pages :
Book Description
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. You will: See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets.
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: Pier Luigi Mazzeo Publisher: BoD – Books on Demand ISBN: 1789851572 Category : Computers Languages : en Pages : 208
Book Description
Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.
Author: Shiho Kim Publisher: Elsevier ISBN: 0128231238 Category : Computers Languages : en Pages : 414
Book Description
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Author: Solomon Negussie Tesema
Author: Solomon Negussie Tesema
Author: Jonathan Greene
Author: Mohammad Motamedi
Author: S Poonkuntran
Author: Eko Cahyono Putro
Author: Umberto Michelucci
Author: Vivienne Sze
Author: Pier Luigi Mazzeo
Author: Ruo Long Lian
Author: Shiho Kim