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Author: Rino Micheloni
Publisher: Springer Nature
ISBN: 303103841X
Category : Technology & Engineering
Languages : en
Pages : 178
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Book Description
This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state drives (SSDs). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called “neuromorphic architecture”), which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.
Author: Rino Micheloni
Publisher: Springer Nature
ISBN: 303103841X
Category : Technology & Engineering
Languages : en
Pages : 178
Get Book Here
Book Description
This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state drives (SSDs). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called “neuromorphic architecture”), which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.
Author: Sudeep Pasricha
Publisher: Springer Nature
ISBN: 303119568X
Category : Technology & Engineering
Languages : en
Pages : 418
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Book Description
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.
Author: Katharina Morik
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110785943
Category : Science
Languages : en
Pages : 506
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Book Description
Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 1 establishes the foundations of this new field (Machine Learning under Resource Constraints). It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Author: Hantao Huang
Publisher: Springer
ISBN: 9811333238
Category : Technology & Engineering
Languages : en
Pages : 157
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Book Description
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
Author: Wen Siang Lew
Publisher: Springer Nature
ISBN: 9811569126
Category : Science
Languages : en
Pages : 439
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Book Description
This book offers a balanced and comprehensive guide to the core principles, fundamental properties, experimental approaches, and state-of-the-art applications of two major groups of emerging non-volatile memory technologies, i.e. spintronics-based devices as well as resistive switching devices, also known as Resistive Random Access Memory (RRAM). The first section presents different types of spintronic-based devices, i.e. magnetic tunnel junction (MTJ), domain wall, and skyrmion memory devices. This section describes how their developments have led to various promising applications, such as microwave oscillators, detectors, magnetic logic, and neuromorphic engineered systems. In the second half of the book, the underlying device physics supported by different experimental observations and modelling of RRAM devices are presented with memory array level implementation. An insight into RRAM desired properties as synaptic element in neuromorphic computing platforms from material and algorithms viewpoint is also discussed with specific example in automatic sound classification framework.
Author: ETCMOS
Publisher: ETCMOS Services Inc.
ISBN: 1927500796
Category :
Languages : en
Pages : 94
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Book Description
Abstracts for presentations at the ETCMOS 2016 conference in Montreal, Canada, May 25 - 27, 2016.
Author: Pieter Harpe
Publisher: Springer Nature
ISBN: 303091741X
Category : Technology & Engineering
Languages : en
Pages : 351
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Book Description
This book is based on the 18 tutorials presented during the 29th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on analog circuits for machine learning, current/voltage/temperature sensors, and high-speed communication via wireless, wireline, or optical links. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.
Author: Panagiotis Dimitrakis
Publisher: Elsevier
ISBN: 0128146303
Category : Technology & Engineering
Languages : en
Pages : 534
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Book Description
Metal Oxides for Non-volatile Memory: Materials, Technology and Applications covers the technology and applications of metal oxides (MOx) in non-volatile memory (NVM) technology. The book addresses all types of NVMs, including floating-gate memories, 3-D memories, charge-trapping memories, quantum-dot memories, resistance switching memories and memristors, Mott memories and transparent memories. Applications of MOx in DRAM technology where they play a crucial role to the DRAM evolution are also addressed. The book offers a broad scope, encompassing discussions of materials properties, deposition methods, design and fabrication, and circuit and system level applications of metal oxides to non-volatile memory. Finally, the book addresses one of the most promising materials that may lead to a solution to the challenges in chip size and capacity for memory technologies, particular for mobile applications and embedded systems. - Systematically covers metal oxides materials and their properties with memory technology applications, including floating-gate memory, 3-D memory, memristors, and much more - Provides an overview on the most relevant deposition methods, including sputtering, CVD, ALD and MBE - Discusses the design and fabrication of metal oxides for wide breadth of non-volatile memory applications from 3-D flash technology, transparent memory and DRAM technology
Author: Yoshio Nishi
Publisher: Woodhead Publishing
ISBN: 0081025858
Category : Technology & Engineering
Languages : en
Pages : 664
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Book Description
Advances in Nonvolatile Memory and Storage Technology, Second Edition, addresses recent developments in the non-volatile memory spectrum, from fundamental understanding, to technological aspects. The book provides up-to-date information on the current memory technologies as related by leading experts in both academia and industry. To reflect the rapidly changing field, many new chapters have been included to feature the latest in RRAM technology, STT-RAM, memristors and more. The new edition describes the emerging technologies including oxide-based ferroelectric memories, MRAM technologies, and 3D memory. Finally, to further widen the discussion on the applications space, neuromorphic computing aspects have been included. This book is a key resource for postgraduate students and academic researchers in physics, materials science and electrical engineering. In addition, it will be a valuable tool for research and development managers concerned with electronics, semiconductors, nanotechnology, solid-state memories, magnetic materials, organic materials and portable electronic devices. - Discusses emerging devices and research trends, such as neuromorphic computing and oxide-based ferroelectric memories - Provides an overview on developing nonvolatile memory and storage technologies and explores their strengths and weaknesses - Examines improvements to flash technology, charge trapping and resistive random access memory
Author: Writam Banerjee
Publisher: CRC Press
ISBN: 1040119107
Category : Technology & Engineering
Languages : en
Pages : 683
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Book Description
In recent years, the abundant advantages of quantum physics, quantum dots, quantum wires, quantum wells, and nanocrystals in various applications have attracted considerable scientific attention in the field of nonvolatile memory (NVM). Nanocrystals are the driving elements that have helped nonvolatile flash memory technology reach its distinguished height, but new approaches are still needed to strengthen nanocrystal-based nonvolatile technology for future applications. This book presents comprehensive knowledge on nanocrystal fabrication methods and applications of nanocrystals in baseline NVM and emerging NVM technologies and the chapters are written by experts in the field from all over the globe. The book presents a detailed analysis on nanocrystal-based emerging devices by a high-level researcher in the field. It has a unique chapter especially dedicated to graphene-based flash memory devices, considering the importance of carbon allotropes in future applications. This updated edition covers emerging ferroelectric memory device, which is a technology for the future, and the chapter is contributed by the well-known Ferroelectric Memory Company, Germany. It includes information related to the applications of emerging memories in sensors and the chapter is contributed by Ajou University, South Korea. The book introduces a new chapter for emerging NVM technology in artificial intelligence and the chapter is contributed by University College London, UK. It guides the readers throughout with appropriate illustrations, excellent figures, and references in each chapter. It is a valuable tool for researchers and developers from the fields of electronics, semiconductors, nanotechnology, materials science, and solid-state memories.
