Download or read book Energy Efficiency and Robustness of Advanced Machine Learning Architectures written by Alberto Marchisio and published by . This book was released on 2024-11-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks.
Download or read book Energy Efficiency and Robustness of Advanced Machine Learning Architectures written by Alberto Marchisio and published by CRC Press. This book was released on 2024-11-14 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.
Download or read book Deep In memory Architectures for Machine Learning written by Mingu Kang and published by Springer Nature. This book was released on 2020-01-30 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.
Download or read book Accelerator Architecture for Secure and Energy Efficient Machine Learning written by Mohammad Hossein Samavatian and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: ML applications are driving the next computing revolution. In this context both performance and security are crucial. We propose hardware/software co-design solutions for addressing both. First, we propose RNNFast, an accelerator for Recurrent Neural Networks (RNNs). RNNs are particularly well suited for machine learning problems in which context is important, such as language translation. RNNFast leverages an emerging class of non-volatile memory called domain-wall memory (DWM). We show that DWM is very well suited for RNN acceleration due to its very high density and low read/write energy. RNNFast is very efficient and highly scalable, with a flexible mapping of logical neurons to RNN hardware blocks. The accelerator is designed to minimize data movement by closely interleaving DWM storage and computation. We compare our design with a state-of-the-art GPGPU and find 21.8X higher performance with 70X lower energy. Second, we brought ML security into ML accelerator design for more efficiency and robustness. Deep Neural Networks (DNNs) are employed in an increasing number of applications, some of which are safety-critical. Unfortunately, DNNs are known to be vulnerable to so-called adversarial attacks. In general, the proposed defenses have high overhead, some require attack-specific re-training of the model or careful tuning to adapt to different attacks. We show that these approaches, while successful for a range of inputs, are insufficient to address stronger, high-confidence adversarial attacks. To address this, we propose HASI and DNNShield, two hardware-accelerated defenses that adapt the strength of the response to the confidence of the adversarial input. Both techniques rely on approximation or random noise deliberately introduced into the model. HASI uses direct noise injection into the model at inference. DNNShield uses approximation that relies on dynamic and random sparsification of the DNN model to achieve inference approximation efficiently and with fine-grain control over the approximation error. Both techniques use the output distribution characteristics of noisy/sparsified inference compared to a baseline output to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50, which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead. We demonstrate a software/hardware-accelerated FPGA prototype, which reduces the performance impact of HASI and DNNShield relative to software-only CPU and GPU implementations.
Download or read book Advanced Machine Learning written by Dr. Amit Kumar Tyagi and published by BPB Publications. This book was released on 2024-06-29 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms. After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. KEY FEATURES ● Basic understanding of machine learning algorithms via MATLAB, R, and Python. ● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies. ● Adding futuristic technologies related to machine learning and deep learning. WHAT YOU WILL LEARN ● Ability to tackle complex machine learning problems. ● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data. ● Efficient data analysis for real-time data will be understood by researchers/ students. ● Using data analysis in near future topics and cutting-edge technologies. WHO THIS BOOK IS FOR This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Statistical Analysis 3. Linear Regression 4. Logistic Regression 5. Decision Trees 6. Random Forest 7. Rule-Based Classifiers 8. Naïve Bayesian Classifier 9. K-Nearest Neighbors Classifiers 10. Support Vector Machine 11. K-Means Clustering 12. Dimensionality Reduction 13. Association Rules Mining and FP Growth 14. Reinforcement Learning 15. Applications of ML Algorithms 16. Applications of Deep Learning 17. Advance Topics and Future Directions
Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Designing Efficient Machine Learning Architectures for Edge Devices written by Tianen Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proliferated on many Internet-of-Things (IoT) applications designed for edge devices. Energy efficiency is one of the most crucial constraints in the design of machine learning applications on IoT devices due to battery and energy-harvesting power sources. Previous attempts use the cloud to transmit data back and forth onto the edge device to alleviate energy strain, but this comes at a great latency and privacy cost. Approximate computing has emerged as a promising solution to bypass the cloud by reducing the energy cost of secure computation ondevice while maintaining high accuracy and low latency. Within machine learning, approximate computing can be used on overparameterized deep neural networks (DNNs) by removing the redundancy by sparsifying the network connections. This thesis attempts to leverage approximate computing techniques on the hardware and software-side of DNNs in order to port onto edge devices with limited power supplies. This thesis aims to implement reconfigurable approximate computing on low-power edge devices, allowing for optimization of the energy-quality tradeoff depending on application specifics. These objectives are achieved by three tasks as follows: i) hardware-side memory-aware logic synthesization, ii) designing energy-aware model compression techniques, and, iii) optimizing edge offloading techniques for efficient client and server communication. These contributions will help facilitate the efficient implementation of edge machine learning on resource-constrained embedded systems.
Download or read book Neuromorphic Computing Systems for Industry 4 0 written by Dhanasekar, S. and published by IGI Global. This book was released on 2023-07-19 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery-powered and deeply embedded devices are challenged to perform AI functions such as computer vision and voice recognition. Microchip Technology Inc., via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain Memory Solution. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory computer solution is ideal for an AI application. Neuromorphic Computing Systems for Industry 4.0 covers the available literature in the field of neural computing-based microchip technology. It provides further research opportunities in this dynamic field. Covering topics such as emotion recognition, biometric authentication, and neural network protection, this premier reference source is an essential resource for technology developers, computer scientists, engineers, students and educators of higher education, librarians, researchers, and academicians.
Download or read book Next Generation Wireless Networks Meet Advanced Machine Learning Applications written by Com?a, Ioan-Sorin and published by IGI Global. This book was released on 2019-01-25 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
Download or read book Neuromorphic Intelligence written by Shuangming Yang and published by Springer Nature. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Energy Conversion and Management written by Giovanni Petrecca and published by Springer. This book was released on 2014-08-07 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overall view of energy conversion and management in industry and in buildings by following the streams of energy from the site boundaries to the end users. Written for an audience of both practitioners and faculty/students, Energy Conversion and Management: Principles and Applications presents general principles of energy conversion and energy sources, both traditional and renewable, in a broad range of facilities such as electrical substations, boiler plants, heat and power plants, electrical networks, thermal fluid distributions lines and insulations, pumps and fans, air compressor systems, cooling plants, HVAC, lighting, and heat recovery plants. The book also examines principles of energy auditing and accounting, the correlation between energy and environment, and includes detail on the economic analysis of energy saving investment and education in the field of energy. This book also: · Explores a broad array of power generation and distribution facilities around the concept of energy conversion, from traditional and renewable sources, correlating many apparently disparate topics · Elucidates fundamental formulas and information-rich figures to help readers in solving any practical energy conversion problems · Emphasizes a holistic perspective on energy conversion and management with a vision of each application as a system beyond its individual elements · Includes a set of Key Performance Index using metrics applicable to energy systems brought into operation over the past 30 years · Gives a set of basic formulas and data that are the essentials of energy conversion and that everybody involved in these fields should perfectly know · Adopts a writing style accessible to technicians and managers in the field of energy conversion while maintaining sufficient rigor and coverage for engineers
Download or read book Embedded Machine Learning for Cyber Physical IoT and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-07 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Download or read book Deep Learning for Radar and Communications Automatic Target Recognition written by Uttam K. Majumder and published by Artech House. This book was released on 2020-07-31 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.
Download or read book Deep Learning Architectures written by Ovidiu Calin and published by Springer Nature. This book was released on 2020-02-13 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
Download or read book The International Conference on Advanced Machine Learning Technologies and Applications AMLTA2018 written by Aboul Ella Hassanien and published by Springer. This book was released on 2018-01-25 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic, security, and intelligence swarms and optimization.
