Design of a memristor-based neuron for spiking neural networks
- Authors: Ostrovskii V.Y.1, Druzhina O.S.1, Kamal O.1, Karimov T.I.1, Butusov D.N.1
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Affiliations:
- Saint Petersburg Electrotechnical University “LETI”
- Issue: Vol 18, No 4 (2023)
- Pages: 827-830
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623428
- DOI: https://doi.org/10.17816/gc623428
- ID: 623428
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Abstract
The primary objective of neuromorphic system design is to surpass limitations in energy efficiency and scaling of classical von Neumann computing systems, through the emulation of animals’ nervous systems. This is achieved by conducting calculations in memory and encoding information in impulse signals, ultimately leading to enhanced adaptability. Adhering to these principles allows for improved energy efficiency and computational speed when solving machine learning problems, encompassing biomedical applications, embedded systems, and cyber-physical systems. Functional blocks modeling the main elements of the central nervous system, namely neurons and synapses, offer an advantage in implementing learning on a chip. The use of memristive electronic components, capable of altering their resistance based on the charge flowing through them, opens new doors for hardware implementation of neuromorphic systems. These devices offer advantages over conventional transistor electronics with respect to power consumption, component density, and performance. To achieve optimal results, the architecture of neuromorphic systems should be optimized at the device level.
Memristive components are utilized to create neurons and synapses. This thesis is specifically focused on producing memristive neuron-like spike signal generators. Previously, memristive neurons were crafted using a locally active element comprised of vanadium dioxide VO2, which incorporated a negative differential resistance section of the IV-curve. One of the recent advancements in this field is a spiking neuron with frequency adaptation [1]. Its drawbacks, however, involve separating the memristive and locally active elements physically, resulting in higher energy consumption and decreased integration quality. In [2], models of memristive neurons with minimal complexity are introduced, which incorporate the Leaky Integrate-and-Fire principle. However, the circuits presented require the application of negative voltage pulses to a DC battery to reset the memristor to its initial high-resistance state. This limitation restricts its sphere of application in neuromorphic systems. This paper proposes a model of a neuron that overcomes these limitations by using the negative differential resistance of the memristor to generate spikes, along with integrating supplementary circuit components to sustain the resistive switching cycles of the memristor.
The neuron model under consideration is implemented using the NI Multisim 14.2 SPICE environment and has been verified in the NI LabVIEW 2022 tool environment. The equations of the modified model of the generalized mean metastable switch of the memristor with self-directed channel [3] represent the current in the memristor branch of the neuron equivalent circuit. The simplicity of the equivalent circuitry of the neuron is attained by merging all the nonlinear features necessary for spike generation into one memristor model. The experimental phase of the study employed obtainable memristors from Knowm Corporation and the laboratory prototyping platform NI ELVIS III. The investigation of the proposed neuron model was accomplished through the application of sinusoidal and rectangular input signals. The refractory time of the neuron model was calculated.
The chosen stack of computer simulation and semi-natural modeling technologies is applied within the research-driven design concept of electronic devices. This approach considers the importance of refining the properties and identification of the design object or its components during the development cycle.
Full Text
The primary objective of neuromorphic system design is to surpass limitations in energy efficiency and scaling of classical von Neumann computing systems, through the emulation of animals’ nervous systems. This is achieved by conducting calculations in memory and encoding information in impulse signals, ultimately leading to enhanced adaptability. Adhering to these principles allows for improved energy efficiency and computational speed when solving machine learning problems, encompassing biomedical applications, embedded systems, and cyber-physical systems. Functional blocks modeling the main elements of the central nervous system, namely neurons and synapses, offer an advantage in implementing learning on a chip. The use of memristive electronic components, capable of altering their resistance based on the charge flowing through them, opens new doors for hardware implementation of neuromorphic systems. These devices offer advantages over conventional transistor electronics with respect to power consumption, component density, and performance. To achieve optimal results, the architecture of neuromorphic systems should be optimized at the device level.
Memristive components are utilized to create neurons and synapses. This thesis is specifically focused on producing memristive neuron-like spike signal generators. Previously, memristive neurons were crafted using a locally active element comprised of vanadium dioxide VO2, which incorporated a negative differential resistance section of the IV-curve. One of the recent advancements in this field is a spiking neuron with frequency adaptation [1]. Its drawbacks, however, involve separating the memristive and locally active elements physically, resulting in higher energy consumption and decreased integration quality. In [2], models of memristive neurons with minimal complexity are introduced, which incorporate the Leaky Integrate-and-Fire principle. However, the circuits presented require the application of negative voltage pulses to a DC battery to reset the memristor to its initial high-resistance state. This limitation restricts its sphere of application in neuromorphic systems. This paper proposes a model of a neuron that overcomes these limitations by using the negative differential resistance of the memristor to generate spikes, along with integrating supplementary circuit components to sustain the resistive switching cycles of the memristor.
The neuron model under consideration is implemented using the NI Multisim 14.2 SPICE environment and has been verified in the NI LabVIEW 2022 tool environment. The equations of the modified model of the generalized mean metastable switch of the memristor with self-directed channel [3] represent the current in the memristor branch of the neuron equivalent circuit. The simplicity of the equivalent circuitry of the neuron is attained by merging all the nonlinear features necessary for spike generation into one memristor model. The experimental phase of the study employed obtainable memristors from Knowm Corporation and the laboratory prototyping platform NI ELVIS III. The investigation of the proposed neuron model was accomplished through the application of sinusoidal and rectangular input signals. The refractory time of the neuron model was calculated.
The chosen stack of computer simulation and semi-natural modeling technologies is applied within the research-driven design concept of electronic devices. This approach considers the importance of refining the properties and identification of the design object or its components during the development cycle.
ADDITIONAL INFORMATION
Authors’ contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, final approval of the version to be published and agree to be accountable for all aspects of the work.
Funding sources. The work is supported by the Russian Science Foundation, project No. 22-19-00573 “Advanced Methods for Identification and Simulation of Dynamical Systems with Nonlinear Components”.
Competing interests. The authors declare that they have no competing interests.
About the authors
V. Yu. Ostrovskii
Saint Petersburg Electrotechnical University “LETI”
Author for correspondence.
Email: vyostrovskii@etu.ru
Russian Federation, Saint Petersburg
O. S. Druzhina
Saint Petersburg Electrotechnical University “LETI”
Email: vyostrovskii@etu.ru
Russian Federation, Saint Petersburg
O. Kamal
Saint Petersburg Electrotechnical University “LETI”
Email: vyostrovskii@etu.ru
Russian Federation, Saint Petersburg
T. I. Karimov
Saint Petersburg Electrotechnical University “LETI”
Email: vyostrovskii@etu.ru
Russian Federation, Saint Petersburg
D. N. Butusov
Saint Petersburg Electrotechnical University “LETI”
Email: vyostrovskii@etu.ru
Russian Federation, Saint Petersburg
References
- Ignatov M, Ziegler M, Hansen M, et al. A memristive spiking neuron with firing rate coding. Front Neurosci. 2015;9:376. doi: 10.3389/fnins.2015.00376
- Kang SM, Choi D, Eshraghian JK, et al. How to build a memristive integrate-and-fire model for spiking neuronal signal generation. IEEE Transactions on Circuits and Systems I: Regular Papers. 2021;68:12:4837–4850. doi: 10.1109/TCSI.2021.3126555
- Ostrovskii V, Fedoseev P, Bobrova Y, Butusov D. Structural and parametric identification of knowm memristors. Nanomaterials (Basel). 2021;12(1):63. doi: 10.3390/nano12010063
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