In-Memory Computing with Emerging Non-volatile Memory for Efficient Processing of Deep Neural Networks
Author :
Publisher :
Page : 0 pages
File Size : 44,19 MB
Release : 2022
Category :
ISBN :
Efficient Processing of Deep Neural Networks
Author : Vivienne Sze
Publisher : Springer Nature
Page : 254 pages
File Size : 10,54 MB
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 3031017668
Book Description
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.
Energy Efficient Hardware Implementation of Neural Networks Using Emerging Non-Volatile Memory Devices
Author : Sangheon Oh
Publisher :
Page : 0 pages
File Size : 25,65 MB
Release : 2023
Category :
ISBN :
Book Description
Deep learning based on neural networks emerged as a robust solution to various complex problems such as speech recognition and visual recognition. Deep learning relies on a great amount of iterative computation on a huge dataset. As we need to transfer a large amount of data and program between the CPU and the memory unit, the data transfer rate through a bus becomes a limiting factor for computing speed, which is known as Von Neumann bottleneck. Moreover, the data transfer between memory and computation spends a large amount of energy and cause significant delay. To overcome the limitation of Von Neumann bottleneck, neuromorphic computing with emerging nonvolatile memory (eNVM) devices has been proposed to perform iterative calculations in memory without transferring data to a processor. This dissertation presents energy efficient hardware implementation of neuromorphic computing applications using phase change memory (PCM), subquantum conductive bridge random access memory (CBRAM), Ag-based CBRAM, and CuOx-based resistive random access memory (RRAM). Although substantial progress has been made towards in-memory computing with synaptic devices, compact nanodevices implementing non-linear activation functions for efficient full-hardware implementation of deep neural networks is still missing. Since DNNs need to have a very large number of activations to achieve high accuracy, it is critical to develop energy and area efficient implementations of activation functions, which can be integrated on the periphery of the synaptic arrays. In this dissertation, we demonstrate a Mott activation neuron that implements the rectified linear unit function in the analogue domain. The integration of Mott activation neurons with a CBRAM crossbar array is also demonstrated in this dissertation.
In-Memory Computing Hardware Accelerators for Data-Intensive Applications
Author : Baker Mohammad
Publisher : Springer Nature
Page : 145 pages
File Size : 34,97 MB
Release : 2023-10-27
Category : Technology & Engineering
ISBN : 303134233X
Book Description
This book describes the state-of-the-art of technology and research on In-Memory Computing Hardware Accelerators for Data-Intensive Applications. The authors discuss how processing-centric computing has become insufficient to meet target requirements and how Memory-centric computing may be better suited for the needs of current applications. This reveals for readers how current and emerging memory technologies are causing a shift in the computing paradigm. The authors do deep-dive discussions on volatile and non-volatile memory technologies, covering their basic memory cell structures, operations, different computational memory designs and the challenges associated with them. Specific case studies and potential applications are provided along with their current status and commercial availability in the market.
In-/Near-Memory Computing
Author : Daichi Fujiki
Publisher : Springer Nature
Page : 124 pages
File Size : 12,25 MB
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 3031017722
Book Description
This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices, without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.
Non-Volatile In-Memory Computing by Spintronics
Author : Hao Yu
Publisher : Morgan & Claypool Publishers
Page : 163 pages
File Size : 33,52 MB
Release : 2016-12-02
Category : Computers
ISBN : 1627056440
Book Description
Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.
Machine Learning and Non-volatile Memories
Author : Rino Micheloni
Publisher : Springer Nature
Page : 178 pages
File Size : 15,11 MB
Release : 2022-05-25
Category : Technology & Engineering
ISBN : 303103841X
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.
Emerging Non-volatile Memory Technologies
Author : Wen Siang Lew
Publisher : Springer Nature
Page : 439 pages
File Size : 21,65 MB
Release : 2021-01-09
Category : Science
ISBN : 9811569126
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.
High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture
Author : Jinshan Yue
Publisher : Springer Nature
Page : 128 pages
File Size : 47,31 MB
Release :
Category :
ISBN : 9819734770
Book Description
Energy-Efficient Devices and Circuits for Neuromorphic Computing
Author : Farooq Ahmad Khanday
Publisher : Elsevier
Page : 0 pages
File Size : 10,23 MB
Release : 2025-10-06
Category : Computers
ISBN : 044329982X
Book Description
In today's world, where the demand for advanced computing systems has skyrocketed, energy efficiency has become a top priority. The development of energy-efficient neuromorphic computing systems has gained significant attention due to their ability to mimic the human brain's low-power, high-performance computing capabilities. The field of neuromorphic computing is at the forefront of research and development in emerging technologies such as artificial intelligence, robotics, and cognitive computing. Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to the field of neuromorphic computing. The book covers a wide range of topics, from the fundamentals of neuron dynamics to the latest developments in energy-efficient CMOS devices and circuits, emerging post-CMOS devices, and non-volatile memory crossbar arrays for energy-efficient neuromorphic computing. It discusses the theoretical analysis of the learning process in spiking neural networks, two-terminal neuromorphic devices, material-engineered neuromorphic devices, and novel biomimetic Si devices for energy-efficient neuromorphic computing architecture. Overall, it will be an essential resource for researchers, engineers, and students working in the fields of neuromorphic computing and energy-efficient electronics.• Comprehensive coverage of neuromorphic computing based upon energy-efficient electronic devices and circuits, providing a deep understanding of the principles and applications of these fields.• Practical guidance and numerous examples, making it an excellent resource for researchers, engineers, and students designing energy-efficient neuromorphic computing systems.• Detailed coverage of emerging post-CMOS devices such as memristors and MTJs and their potential applications in energy-efficient synapses and neurons, providing readers with a cutting-edge perspective on the latest developments in the field
