Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications write by Pethuru Raj. This book was released on 2024-08-22. Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications available in PDF, EPUB and Kindle. The edge AI implementation technologies are fast maturing and stabilizing. Edge AI digitally transforms retail, manufacturing, healthcare, financial services, transportation, telecommunication, and energy. The transformative potential of Edge AI, a pivotal force in driving the evolution from Industry 4.0’s smart manufacturing and automation to Industry 5.0’s human-centric, sustainable innovation. The exploration of the cutting-edge technologies, tools, and applications that enable real-time data processing and intelligent decision-making at the network’s edge, addressing the increasing demand for efficiency, resilience, and personalization in industrial systems. Our book aims to provide readers with a comprehensive understanding of how Edge AI integrates with existing infrastructures, enhances operational capabilities, and fosters a symbiotic relationship between human expertise and machine intelligence. Through detailed case studies, technical insights, and practical guidelines, this book serves as an essential resource for professionals, researchers, and enthusiasts poised to harness the full potential of Edge AI in the rapidly advancing industrial landscape.
Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices
Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices write by Eyal Cidon. This book was released on 2021. Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices available in PDF, EPUB and Kindle. Deep learning models are increasingly being deployed on a vast array of edge devices, including a wide variety of phones, indoor and outdoor cameras, wearable devices and drones. These deep learning models are used for a variety of applications, including real-time speech translation, object recognition and object tracking. The ever-increasing diversity of edge devices, and their limited computational and storage capabilities, have led to significant efforts to optimize ML models for real-time inference on the edge. Yet, inference on the edge still faces two major challenges. First, the same ML model running on different edge devices may produce highly divergent outputs on a nearly identical input. Second, using edge-based models comes at the expense of accuracy relative to larger, cloud-based models. However, attempting to offload data to the cloud for processing consumes excessive bandwidth and adds latency due to constrained and unpredictable wireless network links. This dissertation tackles these two challenges by first characterizing their magnitude, and second, by designing systems that help developers deploy ML models on a wide variety of heterogeneous edge devices, while having the capability to offload data to cloud models. To address the first challenge, we examine the possible root causes for inconsistent efficacy across edge devices. To this end, we measure the variability produced by the device sensors, the device's signal processing hardware and software, and its operating system and processors. We present the first methodical characterization of the variations in model prediction across real-world mobile devices. Counter to prevailing wisdom, we demonstrate that accuracy is not a useful metric to characterize prediction divergence across devices, and introduce a new metric, Instability, which directly captures this variation. We characterize different sources for instability and show that differences in compression formats and image signal processing account for significant instability in object classification models. Notably, in our experiments, 14-17% of images produced divergent classifications across one or more phone models. We then evaluate three different techniques for reducing instability. Building on prior work on making models robust to noise, we design a new technique to fine-tune models to be robust to variations across edge devices. We demonstrate that our fine-tuning techniques reduce instability by 75%. To address the second challenge, of offloading computation to the cloud, we first demonstrate that running deep learning tasks purely on the edge device or purely on the cloud is too restrictive. Instead, we show how we can expand our design space to a modular edge-cloud cooperation scheme. We propose that data collection and distribution mechanisms should be co-designed with the eventual sensing objective. Specifically, we design a modular distributed Deep Neural Network (DNN) architecture that learns end-to-end how to represent the raw sensor data and send it over the network such that it meets the eventual sensing task's needs. Such a design intrinsically adapts to varying network bandwidths between the sensors and the cloud. We design DeepCut, a system that intelligently decides when to offload sensory data to the cloud, combining high accuracy with minimal bandwidth consumption, with no changes to edge and cloud models. DeepCut adapts to the dynamics of both the scene and network and only offloads when necessary and feasible using a lightweight offloading logic. DeepCut can flexibly tune the desired bandwidth utilization, allowing a developer to trade off bandwidth utilization and accuracy. DeepCut achieves results within 10-20% of an offline optimal offloading scheme.
Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications
Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications write by Vahideh Akhlaghi. This book was released on 2020. Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications available in PDF, EPUB and Kindle. Deep Neural Network (DNN) models are now commonly used to automate and optimize complicated tasks in various fields. For improved performance, models increasingly use more processing layers and are frequently over-parameterized. Together these lead to tremendous increases in their compute and memory demands. While these demands can be met in large-scale and accelerated computing environments, they are simply out of reach for the embedded devices seen at the edge of a network and near edge devices such as smart phones and etc. Yet, the demand for moving these (recognition, decision) tasks to edge devices continues to grow for increased localized processing to meet privacy, real-time data processing and decision making needs. Thus, DNNs continue to move towards the edges of the networks at 'edge' or 'near-edge' devices, even though a limited off-chip storage and on-chip memory and logic on the edge devices prohibit the deployment and efficient computation of large yet highly-accurate models. Existing solutions to alleviate such issues improve either the underlying algorithm of these models to reduce their size and computational complexity or the underlying computing architectures to provide efficient computing platforms for these algorithms. While these attempts improve computational efficiency of these models, significant reductions are only possible through optimization of both the algorithms and the hardware for DNNs. In this dissertation, we focus on improving the computation cost of DNN models by taking into account the algorithmic optimization opportunities in the models along with hardware level optimization opportunities and limitations. The techniques proposed in this dissertation lie in two categories: optimal reduction of computation precision and optimal elimination of inessential computation and memory demands. Low precision but low-cost implementation of highly frequent computation through low-cost probabilistic data structures is one of the proposed techniques to reduce the computation cost of DNNs. To eliminate excessive computation that has no more than minimal impact on the accuracy of these models, we propose a software-hardware approach that detects and predicts the outputs of the costly layers with fewer operations. Further, through the design of a machine learning based optimization framework, it has been shown that optimal platform-aware precision reduction at both algorithmic and hardware levels minimizes the computation cost while achieving acceptable accuracy. Finally, inspired by parameter redundancy in over-parameterized models and the limitations of the hardware, reducing the number of parameters of the models through a linear approximation of the parameters from a lower dimensional space is the last approach proposed in this dissertation. We show how a collection of these measures improve deployment of sophisticated DNN models on edge devices.
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry
The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry write by Pethuru R. Chelliah. This book was released on 2023-12-27. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry available in PDF, EPUB and Kindle. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.
Deep Learning on Edge Computing Devices
Deep Learning on Edge Computing Devices - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning on Edge Computing Devices write by Xichuan Zhou. This book was released on 2022-02-02. Deep Learning on Edge Computing Devices available in PDF, EPUB and Kindle. Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization. This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. Focuses on hardware architecture and embedded deep learning, including neural networks Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud Describes how to maximize the performance of deep learning on Edge-computing devices Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
