Early Soft Error Reliability Assessment Of Convolutional Neural Networks Executing On Resource Constrained Iot Edge Devices

This book describes an extensive and consistent soft error assessment of convolutional neural network (CNN) models from different domains through more than 14.8 million fault injections, considering different precision bit-width configurations, optimization parameters, and processor models. The authors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a compiler-based technique w.r.t. traditional redundancy approaches.

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 Neural Network (DNNs) have emerged as an important computational structure that facilitate important tasks such as speech and image recognition, autonomous vehicles, etc. In order to achieve better performance, such as higher classification accuracy, modern DNN models are designed to be more complex in terms of network structure and larger in terms of number of weights in the model. This imposes a great challenge for realizing DNN models on computation devices, especially those resource-constrained devices such as embedded and mobile systems. The challenge arises from three aspects: computation, memory, and energy consumption. First, the number of computations per inference required by modern large and complex DNN models is huge, whereas the computation capability available in the given systems may not be as powerful as a modern GPU or a dedicated processing unit. So, accomplishing the required computation within certain latency is an open challenge. Second, the conflict between the limited on-board memory resource and the static/run-time memory requirement of large DNN models also need to be resolved. Third, the very energy-consuming inference process places a heavy burden on edge devices' battery life. Since the majority of the total energy is consumed by data movement, the goal is not only to fit the DNN model into the system but also to optimize off-chip memory access in order to minimize energy consumption during inference. This dissertation aims to make contributions towards efficient realizations of DNN models on resource-constrained systems. Our contributions can be categorized into three aspects. First, we propose a structure simplification procedure that can identify and eliminate redundant neurons in any layer of a trained DNN model. Once the redundant neurons are identified and removed, the corresponding edges connected to those neurons will be eliminated as well. Then the new weight matrix is calculated directly by our procedure, while retraining may be applied to further recover the lost accuracy if necessary. We also propose a high-level energy model to better explore the tradeoffs in the design space during neuron elimination. Since both the neurons and their edges are eliminated, the memory and energy requirements are also get alleviated. Furthermore, the procedure also allows exploring the tradeoff between model performance and implementation cost. Second, since the convolutional layer is the most energy-consuming and computation heavy layer in Convolutional Neural Networks (CNNs), we propose a structural pruning technique to prune the input channels in convolutional layers. Once the redundant channels are identified and removed, the corresponding convolutional filters will be pruned as well. There significant reduction in static/run-time memory, computation, and energy consumption can be achieved. Moreover, the resulting pruned model is more efficient in terms of network architecture rather than specific weight values, which makes the theoretical reductions of implementation cost much easier to be harvested by existing hardware and software. Third, instead of blindly sending data to cloud and relying on cloud to perform inference, we propose to utilize the computation power of IoT devices to accomplish deep learning tasks while achieving higher degree of customization and privacy level. Specifically, we propose to incorporate a small-sized local customized DNN model to work with a large-sized general DNN model by using a "Mixture of Experts" architecture. Therefore, with minimal implementation overhead, the customized data can be handled by the small-sized DNN to achieve better performance without compromising the performance on general data. Our experiments show that the MoE architecture outperforms popular alternatives such as fine-tuning, bagging, independent ensemble, and multiple choice learning

Today, billions of devices are Internet-connected, IoT standards and protocols are stabilizing, and technical professionals must increasingly solve real problems with IoT technologies. Now, five leading Cisco IoT experts present the first comprehensive, practical reference for making IoT work. IoT Fundamentals brings together knowledge previously available only in white papers, standards documents, and other hard-to-find sources—or nowhere at all. The authors begin with a high-level overview of IoT and introduce key concepts needed to successfully design IoT solutions. Next, they walk through each key technology, protocol, and technical building block that combine into complete IoT solutions. Building on these essentials, they present several detailed use cases, including manufacturing, energy, utilities, smart+connected cities, transportation, mining, and public safety. Whatever your role or existing infrastructure, you’ll gain deep insight what IoT applications can do, and what it takes to deliver them. Fully covers the principles and components of next-generation wireless networks built with Cisco IOT solutions such as IEEE 802.11 (Wi-Fi), IEEE 802.15.4-2015 (Mesh), and LoRaWAN Brings together real-world tips, insights, and best practices for designing and implementing next-generation wireless networks Presents start-to-finish configuration examples for common deployment scenarios Reflects the extensive first-hand experience of Cisco experts

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

In recent years, there has been a remarkable surge in the volume of digital data across various formats and domains. For instance, modern camera systems leverage new technologies and the fusion of information from multiple views to capture high-quality images. As a result of this data explosion, there is a growing interest and demand for analyzing information using data-intensive machine learning algorithms, particularly deep neural networks (DNNs). However, despite the success of deep learning approaches in various domains, their performance on small edge devices with constrained computing power and memory are limited. The primary objective of this thesis is to design efficient intelligent vision systems that effectively overcome the limitations of deep neural networks (DNNs) when deployed on edge devices with limited resources. This work explores a variety of methods aimed at optimizing the utilization of information and context in the design of DNN architectures. By leveraging these techniques, the proposed systems aim to enhance the performance and efficiency of DNNs in resource-constrained environments. Specifically, the thesis proposes context-aware methods to differentiate between low and high quality sensors representations by incorporating the context into the CNN models and reduce the computation and communication costs of edge devices in a distributed camera system. The primary objective is to minimize the computation and communication costs associated with edge devices in a distributed camera system. In addition, the thesis proposes a fault-tolerant mechanism to address the challenges posed by abnormal and noisy data in the system, particularly due to unknown conditions. This mechanism serves as a solution to mitigate the adverse effects of such data, ensuring the reliability and robustness of the proposed system. Furthermore, a resolution-aware multi-view design is outlined to address data transmission and power challenges in embedded devices. Moreover, the thesis introduces a patch-based attention-likelihood technique, designed to enhance the recognition performance of small objects within high-resolution images. This technique effectively reduces the computational burden of handling high-resolution images on edge devices by processing sub-samples of the input patches. By selectively attending to relevant patches, the proposed approach significantly improves the overall efficiency of object recognition while maintaining a high level of accuracy. Finally, the thesis introduces an efficient task-adaptive visual transformer model specifically designed for fine-grained classification tasks on IoT devices. By optimizing the system's performance for IoT devices, it enables efficient and reliable fine-grained classification without compromising computational resources or compromising the accuracy of results. Overall, this thesis offers a comprehensive approach to overcoming the limitations associated with deploying deep neural networks (DNNs) on edge devices within visual intelligent systems.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.