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Memristive Devices For Brain Inspired Computing

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Memristive Devices for Brain Inspired Computing

Memristive Devices for Brain Inspired Computing Book
Author : Sabina Spiga,Abu Sebastian,Damien Querlioz,Bipin Rajendran
Publisher : Woodhead Publishing
Release : 2020-06-12
ISBN : 0081027877
Language : En, Es, Fr & De

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Book Description :

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

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications Book
Author : Jordi Suñé
Publisher : MDPI
Release : 2020-04-09
ISBN : 3039285769
Language : En, Es, Fr & De

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Book Description :

Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

Architectures and Algorithms for Intrinsic Computation with Memristive Devices

Architectures and Algorithms for Intrinsic Computation with Memristive Devices Book
Author : Anonim
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware. My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit non-linear state changes and volatility, I present novel architectures and algorithms that can harness such features for computation. The crossbar architecture is a dense array of memristive devices placed in-between horizontal and vertical nanowires. The regularity of this structure does not inherently provide the means for nonlinear computation of applied input signals. Introducing a modulation scheme that relies on nonlinear memristive device properties, heterogeneous state patterns of applied spatiotemporal input data can be created within the crossbar. In this setup, the untrained and dynamically changing states of the memristive devices offer a useful platform for information processing. Based on the MNIST data set I'll demonstrate how the temporal aspect of memristive state volatility can be utilized to reduce system size and training complexity for high dimensional input data. With 3 times less neurons and 15 times less synapses to train as compared to other memristor-based implementations, I achieve comparable classification rates of up to 93%. Exploiting dynamic state changes rather than precisely tuned stable states, this approach can tolerate device variation up to 6 times higher than reported levels. Random assemblies of memristive networks are analyzed as a substrate for intrinsic computation in connection with reservoir computing; a computational framework that harnesses observations of inherent dynamics within complex networks. Architectural and device level considerations lead to new levels of task complexity, which random memristive networks are now able to solve. A hierarchical design composed of independent random networks benefits from a diverse set of topologies and achieves prediction errors (NRMSE) on the time-series prediction task NARMA-10 as low as 0.15 as compared to 0.35 for an echo state network. Physically plausible network modeling is performed to investigate the relationship between network dynamics and energy consumption. Generally, increased network activity comes at the cost of exponentially increasing energy consumption due to nonlinear voltage-current characteristics of memristive devices. A trade-off, that allows linear scaling of energy consumption, is provided by the hierarchical approach. Rather than designing individual memristive networks with high switching activity, a collection of less dynamic, but independent networks can provide more diverse network activity per unit of energy. My research extends the possibilities of including emerging nanoelectronics into neuromorphic hardware. It establishes memristive devices beyond storage and motivates future research to further embrace memristive device properties that can be linked to different synaptic functions. Pursuing to exploit the functional diversity of memristive devices will lead to novel architectures and algorithms that study rather than dictate the behavior of such devices, with the benefit of creating robust and efficient neuromorphic hardware.

Design of a Neuromemristive Echo State Network Architecture

Design of a Neuromemristive Echo State Network Architecture Book
Author : Qutaiba Mohammed Saleh
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

"Echo state neural networks (ESNs) provide an efficient classification technique for spatiotemporal signals. The feedback connections in the ESN enable feature extraction in both spatial and temporal components in time series data. This property has been used in several application domains such as image and video analysis, anomaly detection, and speech recognition. The software implementations of the ESN demonstrated efficiency in processing such applications, and have low design cost and flexibility. However, hardware implementation is necessary for power constrained resources applications such as therapeutic and mobile devices. Moreover, software realization consumes an order or more power compared to the hardware realization. In this work, a hardware ESN architecture with neuromemristive system is proposed. A neuromemristive system is a brain inspired computing system that uses memristive devises for synaptic plasticity. The memristive devices in neuromemristive systems have several interesting properties such as small footprint, simple device structure, and most importantly zero static power dissipation. The proposed architecture is reconfigurable for different ESN topologies. 2-D mesh architecture and toroidal networks are exploited in the reservoir layer. The relation between performance of the proposed reservoir architecture and reservoir metrics are analyzed. The proposed architecture is tested on a suite of medical and human computer interaction applications. The benchmark suite includes epileptic seizure detection, speech emotion recognition, and electromyography (EMG) based finger motion recognition. The proposed ESN architecture demonstrated an accuracy of 90%, 96%, and 84% for epileptic seizure detection, speech emotion recognition and EMG prosthetic fingers control respectively."--Abstract.

Frontiers in Memristive Materials for Neuromorphic Processing Applications

Frontiers in Memristive Materials for Neuromorphic Processing Applications Book
Author : National Academies of Sciences Engineering and Medicine,Division on Engineering and Physical Sciences,Board on Physics and Astronomy,Condensed Matter and Materials Research Committee
Publisher : Unknown
Release : 2021-09-22
ISBN : 9780309683197
Language : En, Es, Fr & De

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Book Description :

Current von Neumann style computing is energy inefficient and bandwidth limited as information is physically shuttled via electrons between processor, short term non-volatile memory, and long-term storage. Biologically inspired neuromorphic computing, with its inherent autonomous learning capabilities and much lower power requirements based on analog processing, is seen as an avenue for overcoming these limitations. The development of nanoelectronic memory resistors, or memristors, is essential to neuromorphic architectures as they allow logic-based elements for information processing to be combined directly with nonvolatile memory for efficient emulation of neurons and synapses found in the brain. Memristors are typically composed of a switchable material with nonlinear hysteretic behavior sandwiched between two conducting encoding elements. The design, dynamic control, scaling and fundamental understanding of these materials is essential for establishing memristive devices. To explore the state-of-the-art in the materials fundamentally underlying memristor technologies: their science, their mechanisms and their functional imperatives to realize neuromorphic computing machines, the National Academies of Sciences, Engineering, and Medicine's Board on Physics and Astronomy convened a workshop on February 28, 2020. This publication summarizes the presentation and discussion of the workshop.

Enabling Technologies for Very Large Scale Synaptic Electronics

Enabling Technologies for Very Large Scale Synaptic Electronics Book
Author : Themis Prodromakis,Alexantrou Serb
Publisher : Frontiers Media SA
Release : 2018-07-05
ISBN : 2889455084
Language : En, Es, Fr & De

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Book Description :

An important part of the colossal effort associated with the understanding of the brain involves using electronics hardware technology in order to reproduce biological behavior in ‘silico’. The idea revolves around leveraging decades of experience in the electronics industry as well as new biological findings that are employed towards reproducing key behaviors of fundamental elements of the brain (notably neurons and synapses) at far greater speed-scale products than any software-only implementation can achieve for the given level of modelling detail. So far, the field of neuromorphic engineering has proven itself as a major source of innovation towards the ‘silicon brain’ goal, with the methods employed by its community largely focused on circuit design (analogue, digital and mixed signal) and standard, commercial, Complementary Metal-Oxide Silicon (CMOS) technology as the preferred `tools of choice’ when trying to simulate or emulate biological behavior. However, alongside the circuit-oriented sector of the community there exists another community developing new electronic technologies with the express aim of creating advanced devices, beyond the capabilities of CMOS, that can intrinsically simulate neuron- or synapse-like behavior. A notable example concerns nanoelectronic devices responding to well-defined input signals by suitably changing their internal state (‘weight’), thereby exhibiting `synapse-like’ plasticity. This is in stark contrast to circuit-oriented approaches where the `synaptic weight’ variable has to be first stored, typically as charge on a capacitor or digitally, and then appropriately changed via complicated circuitry. The shift of very much complexity from circuitry to devices could potentially be a major enabling factor for very-large scale `synaptic electronics’, particularly if the new devices can be operated at much lower power budgets than their corresponding 'traditional' circuit replacements. To bring this promise to fruition, synergy between the well-established practices of the circuit-oriented approach and the vastness of possibilities opened by the advent of novel nanoelectronic devices with rich internal dynamics is absolutely essential and will create the opportunity for radical innovation in both fields. The result of such synergy can be of potentially staggering impact to the progress of our efforts to both simulate the brain and ultimately understand it. In this Research Topic, we wish to provide an overview of what constitutes state-of-the-art in terms of enabling technologies for very large scale synaptic electronics, with particular stress on innovative nanoelectronic devices and circuit/system design techniques that can facilitate the development of very large scale brain-inspired electronic systems

Hardware Neuromorphic Learning Systems Utilizing Memristive Devices

Hardware Neuromorphic Learning Systems Utilizing Memristive Devices Book
Author : Michael Soltiz
Publisher : Unknown
Release : 2012
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

"As the efficiency of neuromorphic systems improves, biologically-inspired learning techniques are becoming more and more appealing for various computing applications, ranging from pattern and character recognition to general purpose reconfigurable logic. Due to their functional similarities to synapses in the brain, memristors are becoming a key element in the hardware realization of perceptron-based learning systems. By pairing memristive devices with a perceptron-based neuron model, previous work has shown that an efficient and low area neural logic block (NLB) can be developed. However, the use of a simple threshold activation function has limited the set of learnable functions for a single block, resulting in the need for multiple layers to implement certain functions. This complicates the training process, decreases the scalability of the system, and increases the overall energy and delay of large networks. In this work, three novel NLB designs are presented that overcome the limitations of previous hardware NLBs. First, an Adaptive Neural Logic Block (ANLB) and Robust Adaptive Neural Logic Block (RANLB) are proposed. By integrating an adaptive activation function into a perceptron model, these designs are capable of rapidly learning any function in a single layer. Next, a Multi Threshold Neural Logic Block (MTNLB) is proposed in which a static activation function is used to obtain the same functionality with minimal overhead."--Abstract.

Energy Efficient and Error Resilient Neuromorphic Computing in VLSI

Energy Efficient and Error Resilient Neuromorphic Computing in VLSI Book
Author : Yongtae Kim
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Realization of the conventional Von Neumann architecture faces increasing challenges due to growing process variations, device reliability and power consumption. As an appealing architectural solution, brain-inspired neuromorphic computing has drawn a great deal of research interest due to its potential improved scalability and power efficiency, and better suitability in processing complex tasks. Moreover, inherit error resilience in neuromorphic computing allows remarkable power and energy savings by exploiting approximate computing. This dissertation focuses on a scalable and energy efficient neurocomputing architecture which leverages emerging memristor nanodevices and a novel approximate arithmetic for cognitive computing. First, brain-inspired digital neuromorphic processor (DNP) architecture with memristive synaptic crossbar is presented for large scale spiking neural networks. We leverage memristor nanodevices to build an N x N crossbar array to store not only multibit synaptic weight values but also the network configuration data with significantly reduced area cost. Additionally, the crossbar array is accessible both column- and row-wise to significantly expedite the synaptic weight update process for on-chip learning. The proposed digital pulse width modulator (PWM) readily creates a binary pulse with various durations to read and write the multilevel memristors with low cost. Our design integrates N digital leaky integrate-and-fire (LIF) silicon neurons to mimic their biological counterparts and the respective on-chip learning circuits for implementing spike timing dependent plasticity (STDP) learning rules. The proposed column based analog-to-digital conversion (ADC) scheme accumulates the pre-synaptic weights of a neuron efficiently and reduces silicon area by using only one shared arithmetic unit for processing LIF operations of all N neurons. With 256 silicon neurons, the learning circuits and 64K synapses, the power dissipation and area of our design are evaluated as 6.45 mW and 1.86 mm2, respectively, in a 90 nm CMOS technology. Furthermore, arithmetic computations contribute significantly to the overall processing time and power of the proposed architecture. In particular, addition and comparison operations represent 88.5% and 42.9% of processing time and power for digital LIF computation, respectively. Hence, by exploiting the built-in resilience of the presented neuromorphic architecture, we propose novel approximate adder and comparator designs to significantly reduce energy consumption with a very low error rate. The significantly improved error rate and critical path delay stem from a novel carry prediction technique that leverages the information from less significant input bits in a parallel manner. An error magnitude reduction scheme is proposed to further reduce amount of error once detected with low cost in the proposed adder design. Implemented in a commercial 90 nm CMOS process, it is shown that the proposed adder is up to 2.4x faster and 43% more energy efficient over traditional adders while having an error rate of only 0.18%. Additionally, the proposed comparator achieves an error rate of less than 0.1% and an energy reduction of up to 4.9x compared to the conventional ones. The proposed arithmetic has been adopted in a VLSI-based neuromorphic character recognition chip using unsupervised learning. The approximation errors of the proposed arithmetic units have been shown to have negligible impacts on the training process. Moreover, the energy saving of up to 66.5% over traditional arithmetic units is achieved for the neuromorphic chip with scaled supply levels. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151721

The Synthesis of Memristive Neuromorphic Circuits

The Synthesis of Memristive Neuromorphic Circuits Book
Author : Austin Richard Wyer
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

As Moores Law has come to a halt, it has become necessary to explore alternative forms of computation that are not limited in the same ways as traditional CMOS technologies and the Von Neumann architecture. Neuromorphic computing, computing inspired by the human brain with neurons and synapses, has been proposed as one of these alternatives. Memristors, non-volatile devices with adjustable resistances, have emerged as a candidate for implementing neuromorphic computing systems because of their low power and low area overhead. This work presents a C++ simulator for an implementation of a memristive neuromorphic circuit. The simulator is used within a software framework to design and evaluate these circuits. The first chapter provides a background on neuromorphic computing and memristors, explores other neuromorphic circuits and their programming models, and finally presents the software framework for which the simulator was developed. The second chapter presents the C++ simulator and the genetic operators used in the generation of the memristive neuromorphic networks. Next, the third chapter presents a verification of the accuracy of the simulator, and provides some analysis of designs. These analyses focus on variation, the Axon-Hillock neuron model, limited programming resolutions, and online learning mechanisms. Finally, the fourth chapter discusses future considerations. Thus, this thesis presents the C++ simulator as a tool to generate memristive neuromorphic networks. Additionally, it shows how the simulator can be used to understand how the underlying hardware impacts the application level performance of the network.

Circuits and Systems for Biomedical Applications

Circuits and Systems for Biomedical Applications Book
Author : Heidari, Hadi,Ghoreishizadeh, Sara
Publisher : River Publishers
Release : 2018-12-05
ISBN : 8770220530
Language : En, Es, Fr & De

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Book Description :

Circuits and Systems for Biomedical Applications-UKCAS 2018 covers several advanced topics in the area of Devices, Analog and Mixed-Signal Circuits and Systems for Biomedical Applications. The fundamental aspects of these topics are discussed, and state-of-the-art developments are presented. The book proceeds the 1st United Kingdom Circuits and Systems (UKCAS 2018) Workshop. It addresses multidisciplinary theme areas such as Biosensing, Memristors, next-generation medical diagnostics, neural-inspired circuits, neural implants, neuro-prostheses, prosthetic hand and neuro-rehabilitation. Having perceived the device and circuit assets for such technologies and knowing what challenges these present for the biomedical scientists and engineers, integrated circuits for addressable biosensing are reviewed in the first chapter. The Second Chapter is harnessing the power of the brain using metal­oxide Memristors. The third chapter contains construction of an endoscopic capsule for the diagnostics of dysmotilities in the gastro­intestinal track. The next three chapters are on neural interfaces: analogue building blocks of neural inspired circuits are described in the fourth chapter while chapter five focuses on circuits for bio-potential recording from the brain. Networked Integrated circuits and their use in creating advanced implantable stimulation systems will be discussed in chapter six. This topic will be completed by circuits and systems for control of Prosthetic Hands in seventh chapter and genetically enhanced brain­implants for neuro-rehabilitation in chapter eight.