scholarly journals A Parasitic Resistance-Adapted Programming Scheme for Memristor Crossbar-Based Neuromorphic Computing Systems

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4097 ◽  
Author(s):  
Son Ngoc Truong
Keyword(s):  
Modeling Method ◽  
Connection Matrix ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Crossbar Array ◽  
Parasitic Resistance ◽  
Memristor Crossbar ◽  
Resistance Modeling ◽  
The Impact ◽  
Wire Resistance

Memristor crossbar arrays without selector devices, such as complementary-metal oxide semiconductor (CMOS) devices, are a potential for realizing neuromorphic computing systems. However, wire resistance of metal wires is one of the factors that degrade the performance of memristor crossbar circuits. In this work, we propose a wire resistance modeling method and a parasitic resistance-adapted programming scheme to reduce the impact of wire resistance in a memristor crossbar-based neuromorphic computing system. The equivalent wire resistances for the cells are estimated by analyzing the crossbar circuit using the superposition theorem. For the conventional programming scheme, the connection matrix composed of the target memristance values is used for crossbar array programming. In the proposed parasitic resistance-adapted programming scheme, the connection matrix is updated before it is used for crossbar array programming to compensate the equivalent wire resistance. The updated connection matrix is obtained by subtracting the equivalent connection matrix from the original connection matrix. The circuit simulations are performed to test the proposed wire resistance modeling method and the parasitic resistance-adapted programming scheme. The simulation results showed that the discrepancy of the output voltages of the crossbar between the conventional wire resistance modeling method and the proposed wire resistance modeling method is as low as 2.9% when wire resistance varied from 0.5 to 3.0 Ω. The recognition rate of the memristor crossbar with the conventional programming scheme is 99%, 95%, 81%, and 65% when wire resistance is set to be 1.5, 2.0, 2.5, and 3.0 Ω, respectively. By contrast, the memristor crossbar with the proposed parasitic resistance-adapted programming scheme can maintain the recognition as high as 100% when wire resistance is as high as 3.0 Ω.

Achieving Complementary Resistive Switching and Multi-bit Storage Goals by Modulating Dual-ion Reaction through Supercritical Fluid-assisted Ammoniation

Nanoscale ◽  
2021 ◽  
Author(s):  
Lei Li ◽  
Tianjiao Dai ◽  
Kuan-Chang Chang ◽  
Rui Zhang ◽  
Xinnan Lin ◽  
...  
Keyword(s):  
Supercritical Fluid ◽  
Resistive Switching ◽  
Neuromorphic Computing ◽  
Crossbar Array ◽  
Memristor Crossbar

Complementary resistive switching (CRS) is a core requirement in memristor crossbar array construction for neuromorphic computing in view of its capability to avoid sneak path current. However, previous approaches to...

scholarly journals Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61679-61688 ◽  
Author(s):  
Sheng-Yang Sun ◽  
Hui Xu ◽  
Jiwei Li ◽  
Qingjiang Li ◽  
Haijun Liu
Keyword(s):  
Neuromorphic Computing ◽  
Crossbar Array ◽  
Memristor Crossbar

scholarly journals Compensating Circuit to Reduce the Impact of Wire Resistance in a Memristor Crossbar-Based Perceptron Neural Network

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 671 ◽  
Author(s):  
Truong
Keyword(s):  
Neural Network ◽  
Output Voltage ◽  
Recognition Rate ◽  
Metal Wire ◽  
Voltage Variation ◽  
The Wire ◽  
Memristor Crossbar ◽  
The Impact ◽  
Wire Resistance ◽  
Small Voltage

Wire resistance in metal wire is one of the factors that degrade the performance of memristor crossbar circuits. In this paper, an analysis of the impact of wire resistance in a memristor crossbar is performed and a compensating circuit is proposed to reduce the impact of wire resistance in a memristor crossbar-based perceptron neural network. The goal of the analysis is to figure out how wire resistance influences the output voltage of a memristor crossbar. It emerges that the wire resistance on horizontal lines causes the neuron’s output voltage to vary more than the wire resistance on vertical lines. More interesting, the voltage variation caused by wire resistance on horizontal lines increases proportionally to the length of metal wire. The first column has small voltage variation whereas the last column has large voltage variation. In addition, two adjacent columns have almost the same amount of voltage variation. Under these observations, a memristor crossbar-based perceptron neural network with compensating circuit is proposed. The neuron’s outputs of two columns are put into a subtractor circuit to eliminate the voltage variation caused by the wire resistance. The proposed memristor crossbar-based perceptron neural network is trained to recognize the 26 characters. The proposed memristor crossbar shows better recognition rate compared to the previous work when wire resistance is taken into account. The proposed memristor crossbar circuit can maintain the recognition rate as high as 100% when wire resistance is as high as 2.5 Ω. By contrast, the recognition rate of the memristor crossbar without the compensating circuit decreases by 1%, 5%, and 19% when wire resistance is set to be 1.5, 2.0, and 2.5 Ω, respectively.

Security Threat Analyses and Attack Models for Approximate Computing Systems

2021 ◽  
Vol 26 (4) ◽  
pp. 1-31
Author(s):  
Pruthvy Yellu ◽  
Landon Buell ◽  
Miguel Mark ◽  
Michel A. Kinsy ◽  
Dongpeng Xu ◽  
...  
Keyword(s):  
Error Resilience ◽  
Security Threats ◽  
Approximate Computing ◽  
Security Threat ◽  
Security Vulnerabilities ◽  
Computing Systems ◽  
Quantitative Analyses ◽  
Resilience Mechanisms ◽  
The Impact ◽  
Attack Models

Approximate computing (AC) represents a paradigm shift from conventional precise processing to inexact computation but still satisfying the system requirement on accuracy. The rapid progress on the development of diverse AC techniques allows us to apply approximate computing to many computation-intensive applications. However, the utilization of AC techniques could bring in new unique security threats to computing systems. This work does a survey on existing circuit-, architecture-, and compiler-level approximate mechanisms/algorithms, with special emphasis on potential security vulnerabilities. Qualitative and quantitative analyses are performed to assess the impact of the new security threats on AC systems. Moreover, this work proposes four unique visionary attack models, which systematically cover the attacks that build covert channels, compensate approximation errors, terminate normal error resilience mechanisms, and propagate additional errors. To thwart those attacks, this work further offers the guideline of countermeasure designs. Several case studies are provided to illustrate the implementation of the suggested countermeasures.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

Organic synaptic devices based on ionic gel with reduced leakage current

2021 ◽  
Vol 57 (15) ◽  
pp. 1907-1910
Author(s):  
Dapeng Liu ◽  
Yiwei Zhao ◽  
Qianqian Shi ◽  
Shilei Dai ◽  
Li Tian ◽  
...  
Keyword(s):  
Solid State ◽  
Leakage Current ◽  
Dielectric Film ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Current Reduction

A solid-state hybrid electrolyte dielectric film was designed for leakage current reduction, synaptic simulation and neuromorphic computing systems.

Predictive Control and Simulation for Variable-Pitch Wind Turbines

2012 ◽  
Vol 562-564 ◽  
pp. 1012-1015
Author(s):  
S.X. Wang ◽  
Z.X. Li ◽  
D.X. Sun ◽  
X.X. Xie
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Small World ◽  
Modeling Method ◽  
Reference Trajectory ◽  
System Delay ◽  
Variable Pitch ◽  
Electricity Grid ◽  
System Output ◽  
The Impact

In order to avoid the limitations of traditional mechanism modeling method, a neural network (NN) model of variable - pitch wind turbine is built by the NN modeling method based on field data. Then considering that from wind turbine’s startup to grid integration, the generator speed must be controlled to rise to the synchronous speed smoothly and precisely, a neural network model predictive control (NNMPC) strategy based on the small-world optimization algorithm (SWOA) is proposed. Simulation results show that the strategy can forecast the change of generator rotational speed based on the wind speed disturbance, making the controller act ahead to eliminate the impact of system delay. Furthermore, the system output can track the reference trajectory well, making sure that the system can connect the electricity grid steadily.

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