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.
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
Federated Learning
Federated Learning - 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 Federated Learning write by Qiang Yang. This book was released on 2020-11-25. Federated Learning available in PDF, EPUB and Kindle. This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing Applications
Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing 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 Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing Applications write by Yifan Zhuang. This book was released on 2022. Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing Applications available in PDF, EPUB and Kindle. The development of economics and technologies has promoted urbanization worldwide. Urbanization has brought great convenience to daily life. The fast construction of transportation facilities provides various means of transportation for everyday commuting. However, the growing traffic volume has threatened the existing transportation system by raising more traffic safety and congestion issues. Therefore, it is urgent and necessary to implement ITS with dynamic sensing and adjustment abilities. ITS shows great potential to improve traffic safety and efficiency, empowered by advanced IoT and AI. Within this system, the urban sensing and data analysis modules play an essential role in providing primary traffic information for follow-up works, including traffic prediction, operation optimization, and urban planning. Cameras and computer vision algorithms are the most popular toolkit in traffic sensing and analysis tasks. Deep learning-based computer vision algorithms have succeeded in multiple traffic sensing and analysis tasks, e.g., vehicle counting and crowd motion detection. The large-scale deployment of the sensor network and applications of deep learning algorithms significantly magnify previous methods' flaws, which hinder the further expansion of ITS. Firstly, the large-scale sensors and various tasks bring massive data and high workloads for data analysis on central servers. In contrast, annotated data for deep learning training in different tasks is insufficient, which leads to poor generalization when transferring to another application scenario. Additionally, traffic sensing faces adverse conditions with insufficient data and analysis qualities. This dissertation works on proposing efficient and robust machine learning methods for challenging traffic video sensing applications by presenting a systematic and practical workflow to optimize algorithm accuracy and efficiency. This dissertation first considers the high data volume challenge by designing a compression and knowledge distillation pipeline to reduce the model complexity and maintain accuracy. After applying the proposed pipeline, it is possible to further use the optimized algorithm on edge devices. This pipeline also works as the optimization foundation in the remaining works of this dissertation. Besides high data volume for analysis, insufficient training data is a considerable problem when deploying deep learning in practice. This dissertation has focused on two representative scenarios related to public safety – detecting and tracking small-scale persons in crowds and detecting rare objects in autonomous driving. Data augmentation and FSL strategies have been applied to increase the robustness of the machine learning system with limited training data. Finally, traffic sensing targets 24/7 stable operation, even in adverse conditions that reduce visibility and increase image noise with the RGB camera. Sensor fusion by combining RGB and infrared cameras is studied to improve accuracy in all light conditions. In conclusion, urbanization has simultaneously brought opportunities and challenges to the transportation system. ITS shows great potential to take this development chance and handle these challenges. This dissertation works on three data-oriented challenges and improves the accuracy and efficiency of vision-based traffic sensing algorithms. Several ITS applications are explored to demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art accuracy and are far more efficient. In the future, additional research works can be explored based on this dissertation. With the continuing expansion of the sensor network, edge computing will be a more suitable system framework than cloud computing. Binary quantization and hardware-specific operator optimization can contribute to edge computing. Since data insufficiency is common in other transportation applications besides traffic detection, FSL will elevate traffic pattern forecasting and event analysis with a sequence model. For crowd monitoring, the next step will be motion prediction in bird's-eye view based on motion detection results.
Practical Deep Learning for Cloud, Mobile, and Edge
Practical Deep Learning for Cloud, Mobile, and Edge - 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 Practical Deep Learning for Cloud, Mobile, and Edge write by Anirudh Koul. This book was released on 2019-10-14. Practical Deep Learning for Cloud, Mobile, and Edge available in PDF, EPUB and Kindle. Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
