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Deep Learning Model Optimization Deployment And Improvement Techniques For Edge Native Applications
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Book Synopsis Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications by : Pethuru Raj
Download or read book Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications written by Pethuru Raj and published by Cambridge Scholars Publishing. This book was released on 2024-08-22 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices by : Eyal Cidon
Download or read book Improving the Robustness and Accuracy of Deep Learning Deployment on Edge Devices written by Eyal Cidon and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications by : Vahideh Akhlaghi
Download or read book Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications written by Vahideh Akhlaghi and published by . This book was released on 2020 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry by : Pethuru R. Chelliah
Download or read book The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry written by Pethuru R. Chelliah and published by John Wiley & Sons. This book was released on 2023-12-27 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning on Edge Computing Devices by : Xichuan Zhou
Download or read book Deep Learning on Edge Computing Devices written by Xichuan Zhou and published by Elsevier. This book was released on 2022-02-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Optimization for Mobile Deep Learning Applications with Edge Computing by : Yutao Huang
Download or read book Optimization for Mobile Deep Learning Applications with Edge Computing written by Yutao Huang and published by . This book was released on 2018 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of deep learning has attracted the attention from a wide range of fields and brought a large number of related applications. With the rapid growth of mobile computing techniques, numerous deep learning applications are designed for the mobile end. However, since deep learning tasks are computational-intensive, the limited computation resource on the mobile device cannot execute the application effectively. Traditional approach is to push the data and the workload to the remote cloud. Meanwhile, it introduces a high data transmission delay and possibly bottlenecks the overall performance. In this thesis, we apply a new rising concept, edge computing, for mobile deep learning applications. Comparing with cloud learning, the communication delay can be significantly reduced by pushing the workload to the near-end edge. Unlike the existing edge learning frameworks only concerning inference or training, this thesis will focus on both and put forward different optimization approaches towards them. Specifically, the thesis proposes a layer-level partitioning strategy for inference tasks and an edge compression approach with the autoencoder preprocessing for training tasks, to exploit all the available resources from the devices, the edge servers, and the cloud to collaboratively improve the performance for mobile deep learning applications. To further verify the optimization performance in practice, we formulate a scheduling problem for the multi-task execution and propose an efficient heuristic scheduling algorithm. Real-world experiments and extensive simulation tests show that our edge learning framework can achieve up to 70% delay reduction.
Book Synopsis Programming with TensorFlow by : Kolla Bhanu Prakash
Download or read book Programming with TensorFlow written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-01-22 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
Book Synopsis TensorFlow Developer Certification Guide by : Patrick J
Download or read book TensorFlow Developer Certification Guide written by Patrick J and published by GitforGits. This book was released on 2023-08-31 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models. The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more. A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment. The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills. In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications. Key Learnings Comprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning. Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence. In-depth exploration of neural networks, enhancing understanding of model architecture and function. Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts. Detailed insights into model optimization, including regularization, boosting model performance. Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications. Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity. Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios. Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications. Continual emphasis on practical, applicable knowledge, making it suitable for all levels Table of Contents Introduction to Machine Learning and TensorFlow 2.x Up and Running with Neural Networks Building Basic Machine Learning Models Image Recognition with CNN Object Detection Algorithms Text Recognition and Natural Language Processing Strategies to Prevent Overfitting & Underfitting Advanced Neural Networks for NLP Productionizing TensorFlow Models Preparing for TensorFlow Developer Certificate Exam
Book Synopsis Deep Learning Deployment with ONNX and CUDA by : Nate Phoetean
Download or read book Deep Learning Deployment with ONNX and CUDA written by Nate Phoetean and published by Independently Published. This book was released on 2024-04-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the full potential of deep learning with "Deep Learning Deployment with ONNX and CUDA", your comprehensive guide to deploying high-performance AI models across diverse environments. This expertly crafted book navigates the intricate landscape of deep learning deployment, offering in-depth coverage of the pivotal technologies ONNX and CUDA. From optimizing and preparing models for deployment to leveraging accelerated computing for real-time inference, this book equips you with the essential knowledge to bring your deep learning projects to life. Dive into the nuances of model interoperability with ONNX, understand the architecture of CUDA for parallel computing, and explore advanced optimization techniques to enhance model performance. Whether you're deploying to the cloud, edge devices, or mobile platforms, "Deep Learning Deployment with ONNX and CUDA" provides strategic insights into cross-platform deployment, ensuring your models achieve broad accessibility and optimal performance. Designed for data scientists, machine learning engineers, and software developers, this resource assumes a foundational understanding of deep learning, guiding readers through a seamless transition from training to production. Troubleshoot with ease and adopt best practices to stay ahead of deployment challenges. Prepare for the future of deep learning deployment with a closer look at emerging trends and technologies shaping the field. Embrace the future of AI with "Deep Learning Deployment with ONNX and CUDA" - your pathway to deploying efficient, scalable, and robust deep learning models.
Book Synopsis On Edge Empowered Learning Model Optimization for Industrial Applications by : Danyang Song
Download or read book On Edge Empowered Learning Model Optimization for Industrial Applications written by Danyang Song and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As new neural network models continue to expand, Artificial Intelligence (AI) applications seeking more accurate results rely on larger, deeper networks and require powerful computing devices. However, numerous resource-constrained heterogeneous devices are deployed in actual industrial settings. Additionally, due to the high cost of powerful hardware and the heterogeneous hardware system provided by different manufacturers, developing a practical and industrially usable system is challenging. To address these concerns, this thesis first elucidates the use of AI technology for early data analysis in the field of safe driving, demonstrating its potential in industrial settings. Second, a cross-platform model serving framework, LiGo, is developed and verified on actual devices and scenarios to demonstrate its ability to enhance deployment flexibility and efficiency in heterogeneous edge computing.
Book Synopsis AWS Certified Kubernetes on AWS by : Cybellium
Download or read book AWS Certified Kubernetes on AWS written by Cybellium and published by Cybellium . This book was released on with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Book Synopsis Machine Learning in Mobile Edge Computing by : Heting Liu
Download or read book Machine Learning in Mobile Edge Computing written by Heting Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The demand for supporting AI-based applications on mobile devices has been rapidly increasing. To meet this demand, mobile edge computing (MEC) has emerged as a new computing paradigm that enables AI inference at the network edge. Although edge servers offer lower latency, their resources are limited compared to cloud servers. Therefore, effectively managing edge server resources to support edge inference becomes a challenging issue. Additionally, AI-based applications on edge devices generate massive amounts of data that can be utilized for model training by uploading it to the server. However, sharing data poses challenges due to the increasing privacy concerns. Federated learning has emerged as a solution to train models with geographically dispersed edge devices without sharing their local datasets. Due to the limited bandwidth of wireless networks and the heterogeneity of edge devices, enhancing the efficiency of federated learning becomes another challenge in edge computing. The goal of this dissertation is to address these challenges in edge based model training and inference by developing the following techniques. First, we propose a deep reinforcement learning based server selection algorithm to reduce overall system costs when supporting edge inference. We identify the research challenges of server selection in a time-varying MEC system, where the server selection considers system dynamics such as user mobility and server workload. Then we model the server selection decision as a Markov Decision Process, and propose a deep reinforcement learning based algorithm to solve it. Second, we propose techniques to improve the utility of video analytics applications through edge computing. We study the server resource-aware offloading problem for video analytics, and formulate it as an optimization problem, where the goal is to maximize the utility which is a weighted function of accuracy and frame processing rate. We propose an online learning algorithm based on the Bayesian Optimization framework to select server and resolution using local observations, and make it adaptable for time-varying environments. The regret bound of the proposed algorithm is derived, and extensive evaluations are conducted to demonstrate its superior performance. Third, we propose a communication-efficient federated learning framework for heterogeneous edge devices. We identify that gradient quantization should be adaptive to the training process and the clients' communication capability to reduce the training time for heterogeneous clients. We then design an algorithm to minimize the wall-clock training time by exploiting the change of gradient norm to adjust the quantization resolution in each training round to reduce the communication cost while maintaining accuracy. Finally, data heterogeneity at edge devices brings challenges to federated learning. To address this, we propose a dynamic clustering based algorithm for personalized federated learning. Through experiments, we identify that clustering clients with similar data distributions helps address the data heterogeneity problem, and the grouping structure should be adaptive to the training process to improve the model accuracy. We further enhance our algorithm with layer-wise aggregation to both improve model accuracy and reduce communication overhead.
Book Synopsis Machine Learning for Edge Computing by : Amitoj Singh
Download or read book Machine Learning for Edge Computing written by Amitoj Singh and published by Edge AI in Future Computing. This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence. The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needs Amitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.
Book Synopsis Artificial Intelligence and Machine Learning for EDGE Computing by : Rajiv Pandey
Download or read book Artificial Intelligence and Machine Learning for EDGE Computing written by Rajiv Pandey and published by Academic Press. This book was released on 2022-04-26 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
Book Synopsis Efficient Machine Learning Acceleration at the Edge by : Wojciech Romaszkan
Download or read book Efficient Machine Learning Acceleration at the Edge written by Wojciech Romaszkan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: My thesis is a result of a confluence of several trends that have emerged in recent years. First, the rapid proliferation of deep learning across the application and hardware landscapes is creating an immense demand for computing power. Second, the waning of Moore's Law is paving the way for domain-specific acceleration as a means of delivering performance improvements. Third, deep learning's inherent error tolerance is reviving long-forgotten approximate computing paradigms. Fourth, latency, energy, and privacy considerations are increasingly pushing deep learning towards edge inference, with its stringent deployment constraints. All of the above have created a unique, once-in-a-generation opportunity for accelerated widespread adoption of new classes of hardware and algorithms, provided they can deliver fast, efficient, and accurate deep learning inference within a tight area and energy envelope. One approach towards efficient machine learning acceleration that I have explored attempts to push a neural network model size to its absolute minimum. 3PXNet - Pruned, Permuted, Packed XNOR Networks combines two widely used model compression techniques: binarization and sparsity to deliver usable models with a size down to single kilobytes. It uses an innovative combination of weight permutation and packing to create structured sparsity that can be implemented efficiently in both software and hardware. 3PXNet has been deployed as an open-source library targeting microcontroller-class devices with various software optimizations, further improving runtime and storage requirements. The second line of work I have pursued is the application of stochastic computing (SC). It is an approximate, stream-based computing paradigm enabling extremely area-efficient implementations of basic arithmetic operations such as multiplication and addition. SC has been enjoying a renaissance over the past few years due to its unique synergy with deep learning. On the one hand, SC makes it possible to implement extremely dense multiply-accumulate (MAC) computational fabric well suited towards computing large linear algebra kernels, which are the bread-and-butter of deep neural networks. On the other hand, those neural networks exhibit immense approximation tolerance levels, making SC a viable implementation candidate. However, several issues need to be solved to make the SC acceleration of neural networks feasible. The area efficiency comes at the cost of long stream processing latency. The conversion cost between fixed-point and stochastic representations can cancel out the gains from computation efficiency if not managed correctly. The above issues lead to a question on how to design an accelerator architecture that best takes advantage of SC's benefits and minimizes its shortcomings. To address this, I proposed the ACOUSTIC (Accelerating Convolutional Neural Networks through Or-Unipolar Skipped Stochastic Computing) architecture and its extension - GEO (Generation and Execution Optimized Stochastic Computing Accelerator for Neural Networks). ACOUSTIC is an architecture that tries to maximize SC's compute density to amortize conversion costs and memory accesses, delivering system-level reduction in inference energy and latency. It has taped out and demonstrated in silicon, using a 14nm fabrication process. GEO addresses some of the shortcomings of ACOUSTIC. Through the introduction of near-memory computation fabric, GEO enables a more flexible selection of dataflows. Novel progressive buffering scheme unique to SC lowers the reliance on high memory bandwidth. Overall, my work tries to approach accelerator design from the systems perspective, making it stand apart from most recent SC publications targeting point improvements in the computation itself. As an extension to the above line of work, I have explored the combination of SC and sparsity, to apply it to new classes of applications, and enable further benefits. I have proposed the first SC accelerator that supports weight sparsity - SASCHA (Sparsity-Aware Stochastic Computing Hardware Architecture for Neural Network Acceleration), which can improve performance on pruned neural networks, while maintaining the throughput when processing dense ones. SASCHA solves a series of unique, non-trivial challenges of combining SC with sparsity. On the other hand, I have also designed an architecture for accelerating event-based camera object tracking - SCIMITAR. Event-based cameras are relatively new imaging devices which only transmit information about pixels that have changed in brightness, resulting in very high input sparsity. SCIMITAR combines SC with computing-in-memory (CIM), and, through a series of architectural optimizations, is able to take advantage of this new data format to deliver low-latency object detection for tracking applications.
Book Synopsis Cloud-Native DevOps by : Mohammed Ilyas Ahmed
Download or read book Cloud-Native DevOps written by Mohammed Ilyas Ahmed and published by Springer Nature. This book was released on with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Building Machine Learning and Deep Learning Models on Google Cloud Platform by : Ekaba Bisong
Download or read book Building Machine Learning and Deep Learning Models on Google Cloud Platform written by Ekaba Bisong and published by Apress. This book was released on 2019-09-27 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
