79 research outputs found
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
Neuromorphic systems that densely integrate CMOS spiking neurons and
nano-scale memristor synapses open a new avenue of brain-inspired computing.
Existing silicon neurons have molded neural biophysical dynamics but are
incompatible with memristor synapses, or used extra training circuitry thus
eliminating much of the density advantages gained by using memristors, or were
energy inefficient. Here we describe a novel CMOS spiking leaky
integrate-and-fire neuron circuit. Building on a reconfigurable architecture
with a single opamp, the described neuron accommodates a large number of
memristor synapses, and enables online spike timing dependent plasticity (STDP)
learning with optimized power consumption. Simulation results of an 180nm CMOS
design showed 97% power efficiency metric when realizing STDP learning in
10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only
13{\mu}A current consumption when integrating input spikes. Therefore, the
described CMOS neuron contributes a generalized building block for large-scale
brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in
International Joint Conference on Neural Networks (IJCNN) 201
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
Memristors
This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments
Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications
In the last decade a large scientific community has focused on the study of the
memristor. The memristor is thought to be by many the best alternative to CMOS
technology, which is gradually showing its flaws. Transistor technology has developed
fast both under a research and an industrial point of view, reducing the
size of its elements to the nano-scale. It has been possible to generate more and
more complex machinery and to communicate with that same machinery thanks
to the development of programming languages based on combinations of boolean
operands. Alas as shown by Moore’s law, the steep curve of implementation and
of development of CMOS is gradually reaching a plateau. It is clear the need of
studying new elements that can combine the efficiency of transistors and at the same
time increase the complexity of the operations.
Memristors can be described as non-linear resistors capable of maintaining
memory of the resistance state that they reached. From their first theoretical treatment
by Professor Leon O. Chua in 1971, different research groups have devoted their
expertise in studying the both the fabrication and the implementation of this new
promising technology. In the following thesis a complete study on memristors
and memristive elements is presented. The road map that characterizes this study
departs from a deep understanding of the physics that govern memristors, focusing
on the HP model by Dr. Stanley Williams. Other devices such as phase change
memories (PCMs) and memristive biosensors made with Si nano-wires have been
studied, developing emulators and equivalent circuitry, in order to describe their
complex dynamics. This part sets the first milestone of a pathway that passes trough
more complex implementations such as neuromorphic systems and neural networks
based on memristors proving their computing efficiency. Finally it will be presented
a memristror-based technology, covered by patent, demonstrating its efficacy for
clinical applications. The presented system has been designed for detecting and
assessing automatically chronic wounds, a syndrome that affects roughly 2% of
the world population, through a Cellular Automaton which analyzes and processes
digital images of ulcers. Thanks to its precision in measuring the lesions the proposed
solution promises not only to increase healing rates, but also to prevent the worsening
of the wounds that usually lead to amputation and death
Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices
While there is an abundance of research on neural networks that are “inspired” by the brain, few mimic the critical temporal compute features that allow the brain to efficiently perform complex computations. Even fewer methods emulate the heterogeneity of learning produced by biological neurons. Memory devices, such as memristors, are also investigated for their potential to implement neuronal functions in electronic hardware. However, memristors in computing architectures typically operate as non-volatile memories, either as storage or as the weights in a multiply-and-accumulate function that requires direct access to manipulate memristance via a costly learning algorithm. Hence, the integration of memristors into architectures as time-dependent computational units is studied, starting with the development of a compact and versatile mathematical model that is capable of emulating flux-linkage controlled analog (FLCA) memristors and their unique temporal characteristics. The proposed model, which is validated against experimental FLCA LixNbO2 intercalation devices, is used to create memristive circuits that mimic neuronal behavior such as desensitization, paired-pulse facilitation, and spike-timing-dependent plasticity. The model is used to demonstrate building blocks of biomimetic learning via dynamical memristive circuits that implement biomimetic learning rules in a self-training neural network, with dynamical memristive weights that are capable of associative lifelong learning. Successful training of the dynamical memristive neural network to perform image classification of handwritten digits is shown, including lifelong learning by having the dynamical memristive network relearn different characters in succession. An analog computing architecture that learns to associate input-to-input correlations is also introduced, with examples demonstrating image classification and pattern recognition without convolution. The biomimetic functions shown in this paper result from fully ion-driven memristive circuits devoid of integrating capacitors and thus are instructive for exploiting the immense potential of memristive technology for neuromorphic computation in hardware and allowing a common architecture to be applied to a wide range of learning rules, including STDP, magnitude, frequency, and pulse shape among others, to enable an inorganic implementation of the complex heterogeneity of biological neural systems
Theoretical simulations of dynamical systems for advanced reservoir computing applications
There are computational problems that are simply too complex and cannot be handled by traditional CMOS technologies due to practical engineering limitations related to either fundamental physical behavior of devices at small scales, or various energy consumption issues. The field of unconventional computation has emerged as a response to these challenges. Up to date unconventional computation encompasses a plethora of computing frameworks, such as neuromorphic computing, molecular computing, reaction-diffusion computing, or quantum computing, and is ever increasing in its scope. This thesis is biased towards developing sensing applications in the unconventional computing context. This initiative is further extended towards developing novel machine learning applications. The possibility of building intelligent dynamical systems that collect information and analyze it in real-time has been investigated theoretically.The basic idea is to expose a dynamical system to the environment one wishes to analyze over time. The system operates as an environment sensitive reservoir computer. Since the state of the reservoir depends on the environment, the information about the environment one wishes to retrieve gets encoded in the state of the system. The key idea exploited in the thesis is that if the state of the reservoir is highly correlated with the state of the environment\ua0 then the information about the environment can be inferred with a modest engineering overhead. A typical dynamical system is assumed to be a network of environment sensitive elements. Each element can be something simple, but taken together, the elements acquire collective intelligence that can be harvested. These ideas have been examined theoretically (and verified experimentally) by simulating various networks of environment-sensitive elements: the memristor, the capacitor, the constant phase element and the organic field effect transistor element. The simulations were done in the context of ion sensing, which is an extremely complex, many-body, and multi-scale modeling problem
Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs
Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input
Stochastic-Based Computing with Emerging Spin-Based Device Technologies
In this dissertation, analog and emerging device physics is explored to provide a technology platform to design new bio-inspired system and novel architecture. With CMOS approaching the nano-scaling, their physics limits in feature size. Therefore, their physical device characteristics will pose severe challenges to constructing robust digital circuitry. Unlike transistor defects due to fabrication imperfection, quantum-related switching uncertainties will seriously increase their susceptibility to noise, thus rendering the traditional thinking and logic design techniques inadequate. Therefore, the trend of current research objectives is to create a non-Boolean high-level computational model and map it directly to the unique operational properties of new, power efficient, nanoscale devices. The focus of this research is based on two-fold: 1) Investigation of the physical hysteresis switching behaviors of domain wall device. We analyze phenomenon of domain wall device and identify hysteresis behavior with current range. We proposed the Domain-Wall-Motion-based (DWM) NCL circuit that achieves approximately 30x and 8x improvements in energy efficiency and chip layout area, respectively, over its equivalent CMOS design, while maintaining similar delay performance for a one bit full adder. 2) Investigation of the physical stochastic switching behaviors of Mag- netic Tunnel Junction (MTJ) device. With analyzing of stochastic switching behaviors of MTJ, we proposed an innovative stochastic-based architecture for implementing artificial neural network (S-ANN) with both magnetic tunneling junction (MTJ) and domain wall motion (DWM) devices, which enables efficient computing at an ultra-low voltage. For a well-known pattern recognition task, our mixed-model HSPICE simulation results have shown that a 34-neuron S-ANN implementation, when compared with its deterministic-based ANN counterparts implemented with digital and analog CMOS circuits, achieves more than 1.5 ~ 2 orders of magnitude lower energy consumption and 2 ~ 2.5 orders of magnitude less hidden layer chip area
Crossbar-based memristive logic-in-memory architecture
The use of memristors and resistive random access memory (ReRAM) technology to perform logic computations, has drawn considerable attention from researchers in recent years.
However, the topological aspects of the underlying ReRAM architecture and its organization have received less attention, as the focus has mainly been on device-specific properties for functionally complete logic gates through conditional switching in ReRAM
circuits. A careful investigation and optimization of the target geometry is thus highly desirable for the implementation of logic-in-memory architectures. In this paper, we propose a crossbar-based in-memory parallel processing system in which, through the heterogeneity of the resistive cross-point devices, we achieve local information processing in a state-of-the-art ReRAM crossbar architecture with vertical group-accessed transistors as cross-point
selector devices. We primarily focus on the array organization, information storage, and processing flow, while proposing a novel
geometry for the cross-point selection lines to mitigate current sneak-paths during an arbitrary number of possible parallel logic
computations. We prove the proper functioning and potential capabilities of the proposed architecture through SPICE-level circuit simulations of half-adder and sum-of-products logic functions.
We compare certain features of the proposed logic-in-memory approach with another work of the literature, and present an analysis of circuit resources, integration density, and logic computation parallelism.Peer ReviewedPostprint (author's final draft
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