Single Crossbar Array of Memristors With Bipolar Inputs for Neuromorphic Image Recognition

Truong, S.

doi:10.1109/access.2020.2986513

Cited by 12 publications

(17 citation statements)

References 28 publications

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“…The k th output, I k , contained the amount of the similarity between the input vector A and the k th stored pattern. The winner-takes-all circuit was used to finally choose the maximum I k , which showed that the input vector A matched the k th prestored pattern, i.e., k th prestored image [14][15][16].…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…By simplifying the XNOR in Equation ( 4), a new single memristor crossbar array has been shown to be capable of measuring the similarity between two vectors for the application of image recognition [16]. The XNOR function in Equation ( 4) is simplified as Equation ( 6) [16]:…”

Section: The Single Memristor Crossbar Arraymentioning

confidence: 99%

“…In Equation ( 6), A can be dismissed as it has no interaction with the M array. For performing the XNOR function, Equation (6) can be rewritten as Equation ( 7) [16]:…”

Section: The Single Memristor Crossbar Arraymentioning

confidence: 99%

“…For this reason, the twin crossbar architecture consumes less power than the complementary crossbar architecture if and only if the images stored in the crossbar array have the number of '1 bits less than the number of '0 bits, for instance, in DCT compressed images [15]. An up-to-date architecture, the single crossbar architecture, obtained by simplifying the Exclusive-NOR operation, needs only one memristor array for implementing the Exclusive-NOR function in pattern recognition tasks [16]. The complementary and twin crossbar architectures accept unipolar inputs, but the single crossbar array accepts bipolar inputs instead.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the power consumption can be saved in the twin crossbar architecture with DCT compressed images, in which the number of '1 bits is much less than the number of '0 bits [15]. To save area, we can consider the single crossbar architecture, but the unipolar to bipolar circuit must be used in this case [16].…”

Section: Introductionmentioning

confidence: 99%

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Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition

Pham

Truong

2021

Micromachines

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We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neuromorphic image recognition and conducted an experiment to determine the performance of the single crossbar architecture for simple pattern recognition. Ten grayscale images with the size of 32×32 pixels were used for testing and comparing the recognition rates of the three architectures. The recognition rates of the three memristor crossbar architectures were compared to each other when the noise level of images was varied from -10 to 4 dB and the percentage of memristance variation was varied from 0% to 40%. The simulation results showed that the single crossbar architecture had the best Gaussian noise input and memristance variation tolerance in terms of recognition rate. At the signal-to-noise ratio of –10 dB, the single crossbar architecture produced a recognition rate of 91%, which was 2% and 87% higher than those of the twin crossbar architecture and the complementary crossbar architecture, respectively. When the memristance variation percentage reached 40%, the single crossbar architecture had a recognition rate as high as 67.8%, which was 1.8% and 9.8% higher than the recognition rates of the twin crossbar architecture and the complementary crossbar architecture, respectively. Finally, we carried out an experiment to determine the performance of the single crossbar architecture with a fabricated 3 × 3 memristor crossbar based on carbon fiber and aluminum film. The experiment proved successful implementation of pattern recognition with the single crossbar architecture.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: The Single Memristor Crossbar Arraymentioning

confidence: 99%

“…In Equation ( 6), A can be dismissed as it has no interaction with the M array. For performing the XNOR function, Equation (6) can be rewritten as Equation ( 7) [16]:…”

Section: The Single Memristor Crossbar Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition

Pham

Truong

2021

Micromachines

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Artificial sensory system based on memristive devices

Kwon,

Kim,

Kim

et al. 2023

Exploration

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In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real‐time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal‐processing functions such as selective adaption in receptors, leaky integrate‐and‐fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by “artificial receptors,” encoded into spike signals via “artificial neurons,” and integrated and stored through “artificial synapses.” The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high‐performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real‐time detection and processing of environmental information. This review explores the recent advances in memristor‐based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in‐depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor‐based artificial sensory systems are discussed.

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OxRAM + OTS optimization for binarized neural network hardware implementation

Lopez¹,

Hirtzlin²,

Dampfhoffer³

et al. 2021

Semicond. Sci. Technol.

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Low-power memristive devices embedded on graphics or central processing units logic core are a very promising non-von-Neumann approach to improve significantly the speed and power consumption of deep learning accelerators, enhancing their deployment on embedded systems. Among various non-ideal emerging neuromorphic memory devices, synaptic weight hardware implementation using resistive random-access memories (RRAMs) within 1T1R architectures promises high performance on low precision binarized neural networks (BNN). Taking advantage of the RRAM capabilities and allowing to substantially improve the density thanks to the ovonic threshold selector (OTS) selector, this work proposes to replace the standard 1T1R architecture with a denser 1S1R crossbar system, where an HfO2-based resistive oxide memory (OxRAM) is co-integrated with a Ge-Se-Sb-N-based OTS. In this context, an extensive experimental study is performed to optimize the 1S1R stack and programming conditions for extended read window margin and endurance characteristics. Focusing on the standard machine learning MNIST image recognition task, we perform offline training simulations in order to define the constraints on the devices during the training process. A very promising bit error rate of ∼10−3 is demonstrated together with 1S1R 104 error-free programming endurance characteristics, fulfilling the requirements for the application of interest. Based on this simulation and experimental study, BNN figures of merit (system footprint, number of weight updates, accuracy, inference speed, electrical consumption per image classification and tolerance to errors) are optimized by engineering the number of learnable parameters of the system. Altogether, an inherent BNN resilience to 1S1R parasitic bit errors is demonstrated.

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Single Crossbar Array of Memristors With Bipolar Inputs for Neuromorphic Image Recognition

Cited by 12 publications

References 28 publications

Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition

Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition

Artificial sensory system based on memristive devices

OxRAM + OTS optimization for binarized neural network hardware implementation

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