127 research outputs found
A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning
Spiking Neural Network (SNN) naturally inspires hardware implementation as it
is based on biology. For learning, spike time dependent plasticity (STDP) may
be implemented using an energy efficient waveform superposition on memristor
based synapse. However, system level implementation has three challenges.
First, a classic dilemma is that recognition requires current reading for short
voltagespikes which is disturbed by large voltagewaveforms that are
simultaneously applied on the same memristor for realtime learning i.e. the
simultaneous readwrite dilemma. Second, the hardware needs to exactly
replicate software implementation for easy adaptation of algorithm to hardware.
Third, the devices used in hardware simulations must be realistic. In this
paper, we present an approach to address the above concerns. First, the
learning and recognition occurs in separate arrays simultaneously in
realtime, asynchronously avoiding nonbiomimetic clocking based
complex signal management. Second, we show that the hardware emulates software
at every stage by comparison of SPICE (circuitsimulator) with MATLAB
(mathematical SNN algorithm implementation in software) implementations. As an
example, the hardware shows 97.5 per cent accuracy in classification which is
equivalent to software for a FisherIris dataset. Third, the STDP is
implemented using a model of synaptic device implemented using HfO2 memristor.
We show that an increasingly realistic memristor model slightly reduces the
hardware performance (85 per cent), which highlights the need to engineer RRAM
characteristics specifically for SNN.Comment: Eight pages, ten figures and two table
SPICE Simulation of RRAM-Based Cross-Point Arrays Using the Dynamic Memdiode Model
We thoroughly investigate the performance of the Dynamic Memdiode Model (DMM) when used for simulating the synaptic weights in large RRAM-based cross-point arrays (CPA) intended for neuromorphic computing. The DMM is in line with Prof. Chua’s memristive devices theory, in which the hysteresis phenomenon in electroformed metal-insulator-metal structures is represented by means of two coupled equations: one equation for the current-voltage characteristic of the device based on an extension of the quantum point-contact (QPC) model for dielectric breakdown and a second equation for the memory state, responsible for keeping track of the previous history of the device. By considering ex-situ training of the CPA aimed at classifying the handwritten characters of the MNIST database, we evaluate the performance of a Write-Verify iterative scheme for setting the crosspoint conductances to their target values. The total programming time, the programming error, and the inference accuracy obtained with such writing scheme are investigated in depth. The role played by parasitic components such as the line resistance as well as some CPA’s particular features like the dynamical range of the memdiodes are discussed. The interrelationship between the frequency and amplitude values of the write pulses is explored in detail. In addition, the effect of the resistance shift for the case of a CPA programmed with no errors is studied for a variety of input signals, providing a design guideline for selecting the appropriate pulse’s amplitude and frequency.Fil: Aguirre, Fernando Leonel. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Pazos, Sebastián MatÃas. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Palumbo, Félix Roberto Mario. Consejo Nacional de Investigaciones CientÃficas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; ArgentinaFil: Suñé, Jordi. Universitat Autònoma de Barcelona; EspañaFil: Miranda, Enrique. Universitat Autònoma de Barcelona; Españ
Assessment and Improvement of the Pattern Recognition Performance of Memdiode-Based Cross-Point Arrays with Randomly Distributed Stuck-at-Faults
In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive crosspoint array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor’s electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated.In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive cross-point array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor?s electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated.Fil: Aguirre, Fernando Leonel. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; Argentina. Universitat Autònoma de Barcelona; España. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Pazos, Sebastián MatÃas. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Palumbo, Félix Roberto Mario. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las IngenierÃas; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Morell, Antoni. Universitat Autònoma de Barcelona; EspañaFil: Suñé, Jordi. Universitat Autònoma de Barcelona; EspañaFil: Miranda, Enrique. Universitat Autònoma de Barcelona; Españ
Memristive Anodic Oxides: Production, Properties and Applications in Neuromorphic Computing
Memristive devices generally consist of metal oxide elements with specific structure and chemical composition, which are crucial to obtain the required variability in resistance. This makes the control of oxide properties vital. While CMOS compatible production technologies for metal oxides deposition generally involve physical or chemical deposition pathways, we here describe the possibility of using an electrochemical technique, anodic oxidation, as an alternative route to produce memristive oxides. In fact, anodization allows to form a very large range of oxides on the surface of valve metals, such as titanium, hafnium, niobium and tantalum, whose thickness, structure and functional properties depend on process parameters imposed. These oxides may be of interest to build neural networks based on memristive elements produced by anodic oxidation
Resistive switching in ALD metal-oxides with engineered interfaces
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