Neuro Inspired Computing Using Emerging Non Volatile Memories

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Neuro Inspired Computing Using Emerging Non Volatile Memories

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Author : Yuhan Shi
language : en
Publisher:
Release Date : 2023

Neuro Inspired Computing Using Emerging Non Volatile Memories written by Yuhan Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Data movement between separate processing and memory units in traditional von Neumann computing systems is costly in terms of time and energy. The problem is aggravated by the recent explosive growth in data intensive applications related to artificial intelligence. In-memory computing has been proposed as an alternative approach where computational tasks can be performed directly in memory without shuttling back and forth between the processing and memory units. Memory is at the heart of in-memory computing. Technology scaling of mainstream memory technologies, such as static random-access memory (SRAM) and Dynamic random-access memory (DRAM), is increasingly constrained by fundamental technology limits. The recent research progress of various emerging nonvolatile memory (eNVM) device technologies, such as resistive random-access memory (RRAM), phase-change memory (PCM), conductive bridging random-access memory (CBRAM), ferroelectric random-access memory (FeRAM) and spin-transfer torque magnetoresistive random-access memory (STT-MRAM), have drawn tremendous attentions owing to its high speed, low cost, excellent scalability, enhanced storage density. Moreover, an eNVM based crossbar array can perform in-memory matrix vector multiplications in analog manner with high energy efficiency and provide potential opportunities for accelerating computation in various fields such as deep learning, scientific computing and computer vision. This dissertation presents research work on demonstrating a wide range of emerging memory device technologies (CBRAM, RRAM and STT-MRAM) for implementing neuro-inspired in-memory computing in several real-world applications using software and hardware co-design approach. Chapter 1 presents low energy subquantum CBRAM devices and a network pruning technique to reduce network-level energy consumption by hundreds to thousands fold. We showed low energy (10×-100× less than conventional memory technologies) and gradual switching characteristics of CBRAM as synaptic devices. We developed a network pruning algorithm that can be employed during spiking neural network (SNN) training to further reduce the energy by 10×. Using a 512 Kbit subquantum CBRAM array, we experimentally demonstrated high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Chapter 2 presents the details of SNN pruning algorithm that used in Chapter1. The pruning algorithms exploits the features of network weights and prune weights during the training based on neurons' spiking characteristics, leading significant energy saving when implemented in eNVM based in-memory computing hardware. Chapter 3 presents a benchmarking analysis for the potential use of STT-MRAM in in-memory computing against SRAM at deeply scaled technology nodes (14nm and 7nm). A C++ based benchmarking platform is developed and uses LeNet-5, a popular convolutional neural network model (CNN). The platform maps STT-MRAM based in-memory computing architectures to LeNet-5 and can estimate inference accuracy, energy, latency, and area accurately for proposed architectures at different technology nodes compared against SRAM. Chapter 4 presents an adaptive quantization technique that compensates the accuracy loss due to limited conductance levels of PCM based synaptic devices and enables high-accuracy SNN unsupervised learning with low-precision PCM devices. The proposed adaptive quantization technique uses software and hardware co-design approach by designing software algorithms with consideration of real synaptic device characteristics and hardware limitations. Chapter 5 presents a real-world neural engineering application using in-memory computing. It presents an interface between eNVM based crossbar with neural electrodes to implement a real-time and high-energy efficient in-memory spike sorting system. A real-time hardware demonstration is performed using CuOx based eNVM crossbar to sort spike data in different brain regions recorded from multi-electrode arrays in animal experiments, which further extend the eNVM memory technologies for neural engineering applications. Chapter 6 presents a real-world deep learning application using in-memory computing. We demonstrated a direct integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott-ReLU activation neurons for scalable, energy and area efficient hardware implementation of DNNs. Chapter 7 is the conclusion of this dissertation. The future directions of in-memory computing system based on eNVM technologies are discussed.

Memristive Devices For Brain Inspired Computing

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Author : Sabina Spiga
language : en
Publisher: Woodhead Publishing
Release Date : 2020-06-12

Memristive Devices For Brain Inspired Computing written by Sabina Spiga and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Technology & Engineering categories.

Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications—Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists. - Provides readers an overview of four key concepts in this emerging research topic including materials and device aspects, algorithmic aspects, circuits and architectures and target applications - Covers a broad range of applications, including brain-inspired computing, computational memory, deep learning and spiking neural networks - Includes perspectives from a wide range of disciplines, including materials science, electrical engineering and computing, providing a unique interdisciplinary look at the field

Journal Of The Royal Society Interface

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Author :
language : en
Publisher:
Release Date : 2006

Journal Of The Royal Society Interface written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Life sciences categories.

In Memory Computing With Emerging Non Volatile Memory For Efficient Processing Of Deep Neural Networks

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Author :
language : en
Publisher:
Release Date : 2022

In Memory Computing With Emerging Non Volatile Memory For Efficient Processing Of Deep Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Microneuro 99 Proceedings Of The Seventh International Conference On Microelectronics For Neural Fuzzy And Bio Inspired Systems April 7 9 1999 Granada Spain

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Author : Universidad de Granada
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1999

Microneuro 99 Proceedings Of The Seventh International Conference On Microelectronics For Neural Fuzzy And Bio Inspired Systems April 7 9 1999 Granada Spain written by Universidad de Granada and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.

Organized by the U. of Granada, the April 1999 conference focused on applications of bio-inspired microelectronic circuits and systems. Published as extended papers, the contributions represent five invited talks (on various phenomena-to-emerging-technology advances), a demonstration of the autonomo

Resistive Random Access Memory Rram

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Author : Shimeng Yu
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Resistive Random Access Memory Rram written by Shimeng Yu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.

RRAM technology has made significant progress in the past decade as a competitive candidate for the next generation non-volatile memory (NVM). This lecture is a comprehensive tutorial of metal oxide-based RRAM technology from device fabrication to array architecture design. State-of-the-art RRAM device performances, characterization, and modeling techniques are summarized, and the design considerations of the RRAM integration to large-scale array with peripheral circuits are discussed. Chapter 2 introduces the RRAM device fabrication techniques and methods to eliminate the forming process, and will show its scalability down to sub-10 nm regime. Then the device performances such as programming speed, variability control, and multi-level operation are presented, and finally the reliability issues such as cycling endurance and data retention are discussed. Chapter 3 discusses the RRAM physical mechanism, and the materials characterization techniques to observe the conductive filaments and the electrical characterization techniques to study the electronic conduction processes. It also presents the numerical device modeling techniques for simulating the evolution of the conductive filaments as well as the compact device modeling techniques for circuit-level design. Chapter 4 discusses the two common RRAM array architectures for large-scale integration: one-transistor-one-resistor (1T1R) and cross-point architecture with selector. The write/read schemes are presented and the peripheral circuitry design considerations are discussed. Finally, a 3D integration approach is introduced for building ultra-high density RRAM array. Chapter 5 is a brief summary and will give an outlook for RRAM’s potential novel applications beyond the NVM applications.

Progress In Connectionist Based Information Systems

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Author :
language : en
Publisher:
Release Date : 1998

Progress In Connectionist Based Information Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.

These two volumes consist of about 350 papers in three main areas of artificial intelligence and neurocomputing, namely: (1) modelling the brain; (2) methods of soft computing; (3) applications of intelligent information systems. The materials, contained in two volumes, emphasise the importance of connectionist-based information systems which use neural networks and other methods to achieve intelligent information processing, such as speech recognition and language understanding, pattern recognition, vision, learning and adaptation, planning, and decision making. Some of the methods of the connectionist-based information systems directly model the physical organisation of the human brain, which is the area of brain-like computing. Other methods model cognitive aspects of human behaviours, which is the area of cognitive engineering. A third group of methods are based on statistical and probability theory. All these methods are presented and applied on concrete problems. Many connectionist-based systems are described in different papers of the two volumes.These two volumes are a comprehensive and up-to-date guide to the diverse topics of neuro-computing, artificial intelligence and knowledge engineering.

Energy Efficient Hardware Implementation Of Neural Networks Using Emerging Non Volatile Memory Devices

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Author : Sangheon Oh
language : en
Publisher:
Release Date : 2023

Energy Efficient Hardware Implementation Of Neural Networks Using Emerging Non Volatile Memory Devices written by Sangheon Oh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

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.

Ip Strategy

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Author :
language : en
Publisher:
Release Date : 2009

Ip Strategy written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Intellectual property categories.

Santa Clara Computer And High Technology Law Journal

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Author :
language : en
Publisher:
Release Date : 1991

Santa Clara Computer And High Technology Law Journal written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.