The use of memristive devices in machine vision systems
- Authors: Shchanikov S.A.1
-
Affiliations:
- Murom Institute of Vladimir State University, Vladimir State University named after Alexander and Nikolay Stoletov
- Issue: Vol 18, No 4 (2023)
- Pages: 831-834
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623429
- DOI: https://doi.org/10.17816/gc623429
- ID: 623429
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Abstract
The comparison results of processing units with memristive devices versus modern hardware accelerators of artificial neural networks (ANNs) based on traditional electronic components, as presented in the review [1], demonstrate numerous advantages across all major indicators such as throughput, energy efficiency, accuracy, and others. This report analyzes the current state of memristive devices in addressing machine vision issues. Special attention is paid to the concept of [2] neuromorphic machine vision systems (MVS) based on memristive devices. This concept’s distinct feature lies in its fully analog system, commencing from information input to its output. It encompasses sensory and neural components. The sensory part is responsible for gathering visual information and transferring it to the neural segment for processing through the ANN model algorithm.
A specific instance for implementing the input channel of the sensor component involves connecting a photodiode (PD) and a memristor in a single circuit. When the circuit is flipped in reverse bias and light falls on the PD, a photocurrent flows through it from the cathode to the anode. Depending on the light intensity and exposure time, this photocurrent alters the resistance of the memristor, thereby converting illumination into resistance. If visual information doesn’t require encoding with memristor resistances, they can be replaced with a load resistance of the same nominal value for all channels. Irrespective of the input channel variant, the signal encoding visual information is fed into the neural part without digitization. Memristors act as synapses as part of the neural part. They can be used to implement synapses in both traditional formal ANN architectures, where input information is multiplied by a pre-programmed weight, and synapses for spiking neural networks, where a memristor exhibits synaptic plasticity mechanisms similar to those in biological neural networks [3].
If the output from the suggested MVS input channel variants is connected to a device that operates on the “integrate and fire” principle, the device can be deemed as not only an input for a structured ANN, but also a presynaptic neuron for a spiking ANN. The neuron’s frequency of spikes will depend on the light intensity; the brighter the light, the higher the frequency of spikes and vice versa. Faster charge accumulation occurs in channels with low resistance. The complete analog machine vision system will function as a spike neural network, without incorporating any analog-to-digital or digital-to-analog converters. Compared to digital machine vision systems, this approach will significantly decrease energy consumption while producing wearable and on-board electronics with distinct tactical and technical features. This design can be tailored to the size of modern matrices of photo and video fixation devices and employed as a hardware accelerator for the ANN models currently used to process images, and it can serve as a foundation for advancing this area.
Full Text
The comparison results of processing units with memristive devices versus modern hardware accelerators of artificial neural networks (ANNs) based on traditional electronic components, as presented in the review [1], demonstrate numerous advantages across all major indicators such as throughput, energy efficiency, accuracy, and others. This report analyzes the current state of memristive devices in addressing machine vision issues. Special attention is paid to the concept of [2] neuromorphic machine vision systems (MVS) based on memristive devices. This concept’s distinct feature lies in its fully analog system, commencing from information input to its output. It encompasses sensory and neural components. The sensory part is responsible for gathering visual information and transferring it to the neural segment for processing through the ANN model algorithm.
A specific instance for implementing the input channel of the sensor component involves connecting a photodiode (PD) and a memristor in a single circuit. When the circuit is flipped in reverse bias and light falls on the PD, a photocurrent flows through it from the cathode to the anode. Depending on the light intensity and exposure time, this photocurrent alters the resistance of the memristor, thereby converting illumination into resistance. If visual information doesn’t require encoding with memristor resistances, they can be replaced with a load resistance of the same nominal value for all channels. Irrespective of the input channel variant, the signal encoding visual information is fed into the neural part without digitization. Memristors act as synapses as part of the neural part. They can be used to implement synapses in both traditional formal ANN architectures, where input information is multiplied by a pre-programmed weight, and synapses for spiking neural networks, where a memristor exhibits synaptic plasticity mechanisms similar to those in biological neural networks [3].
If the output from the suggested MVS input channel variants is connected to a device that operates on the “integrate and fire” principle, the device can be deemed as not only an input for a structured ANN, but also a presynaptic neuron for a spiking ANN. The neuron’s frequency of spikes will depend on the light intensity; the brighter the light, the higher the frequency of spikes and vice versa. Faster charge accumulation occurs in channels with low resistance. The complete analog machine vision system will function as a spike neural network, without incorporating any analog-to-digital or digital-to-analog converters. Compared to digital machine vision systems, this approach will significantly decrease energy consumption while producing wearable and on-board electronics with distinct tactical and technical features. This design can be tailored to the size of modern matrices of photo and video fixation devices and employed as a hardware accelerator for the ANN models currently used to process images, and it can serve as a foundation for advancing this area.
ADDITIONAL INFORMATION
Funding sources. The study was supported by the grant of the Russian Science Foundation No. 21-71-00136 “Development of Scientific and Technological Principles for the Creation and Functioning of Neuromorphic Analog Machine Vision Systems based on Memristive Devices”.
Competing interests. The author declares that he has no competing interests.
About the authors
S. A. Shchanikov
Murom Institute of Vladimir State University, Vladimir State University named after Alexander and Nikolay Stoletov
Author for correspondence.
Email: seach@inbox.ru
Russian Federation, Murom
References
- Amirsoleimani A, Alibart F, Yon V, et al. In memory vector matrix multiplication in monolithic complementary metal–oxide–semiconductor memristor integrated circuits: design choices, challenges, and perspectives. Advanced Intelligent Systems. 2020;2(11):2000115. doi: 10.1002/aisy.202000115
- Shchanikov S, Bordanov I. The concept of neuromorphic vision systems based on memristive devices. 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA); 2022 Sept 14–16; IEEE. P. 256–259. doi: 10.1109/DCNA56428.2022.9923295
- Koryazhkina M, Okulich E, Ryabova M. Effect of pulse amplitude on depression and potentiation of ZrO 2 (Y)-based memristive synaptic device. 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA); 2022 Sept 14–16; IEEE. P. 147–150. doi: 10.1109/DCNA56428.2022.9923189
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