Principles of analog neuromorphic computing: from components to systems and algorithms

V. A. Demin; Демин В. А.; A. V. Emelyanov; Емельянов А. В.; K. E. Nikiruy; Никируй К. Е.; I. A. Surazhevsky; Суражевский И. А.; A. V. Sitnikov; Ситников А. В.; V. V. Rylkov; Рыльков В. В.; P. K. Kashkarov; Кашкаров П. К.; M. V. Kovalchuk; Ковальчук М. В.

doi:10.17816/gc623345

Principles of analog neuromorphic computing: from components to systems and algorithms

作者: Demin V.A.¹, Emelyanov A.V.¹^,2, Nikiruy K.E.¹, Surazhevsky I.A.¹, Sitnikov A.V.¹^,3, Rylkov V.V.¹, Kashkarov P.K.¹^,2, Kovalchuk M.V.¹^,2^,4
隶属关系:
1. National Research Centre “Kurchatov Institute”
2. The Moscow Institute of Physics and Technology
3. Voronezh State Technical University
4. Lomonosov Moscow State University
期: 卷 18, 编号 4 (2023)
页面: 794-797
栏目: Conference proceedings
URL: https://journals.rcsi.science/2313-1829/article/view/256364
DOI: https://doi.org/10.17816/gc623345
ID: 256364

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This report presents the current state of affairs in the implementation of artificial intelligence hardware accelerators based on practically successful neural network algorithms of the first and second generations based on formal artificial neural networks (ANNs). The shortcomings of existing solutions are noted and ways to overcome them using analog neuromorphic architectures are outlined.

The latter are created on the principles of the structuring and functioning of a living nervous system, using artificial neurons and models of synaptic contacts - the so–called memristors, electrically rewritable nanoscale elements of non-volatile memory [1-3]. With the use of these elements, it is possible to significantly increase the performance and energy efficiency of algorithm accelerators based on the ANNs [4-6], as well as the formation of promising computing systems based on bioplausible 3rd generation neural network algorithms - Spiking Neural Networks (SNNs) [7-9].

The original method of substantiating the optimal rules for local tuning SNNs with frequency encoding and the possibility of their implementation in the form of the Spike-Timing-Dependent Plasicity (STDP) are discussed [10]. The results of SNN learning stability to a variability of analog memristors, as well as the use of noise as a constructive factor in the fine-tuning and maintenance of SNN memristive weights are demonstrated [7, 11].

Also, approaches to the implementation of local plasticity rules with dopamine-like modulation as a type of SNN reinforcement learning are discussed. The latter is necessary for the formation of imitative "needs" of an agent in the process of its autonomous functioning [12, 13, 14]. The first results on the creation of a prototype of a memristive implantable neuroprosthesis of the motor activity are considered [15, 16].

Finally, possible hardware solutions for both neuronal elements and synaptic connections based on suitable memristive devices are demonstrated. The concept and first results on the creation of an analog neuromorphic computing system based on the above components are presented.

Thus, an attempt is made to systematize the existing and original methods of implementing energy-efficient and compact analog neuromorphic computing systems for real-time and life-learning artificial intelligence.

关键词

neuromorphic computing, memristor, spiking neural networks, STDP, unsupervised learning, dopamine-like reinforcement learning, neurohybrid systems

全文:

This report outlines the current implementation status of hardware accelerators for artificial intelligence, focusing on successful neural network algorithms of the first and second generations that use formal artificial neural networks (ANNs). Identified shortcomings of current solutions are addressed, with proposed solutions using analog neuromorphic architectures.

The latter are designed based on the structural and functional principles of a living nervous system, using artificial neurons and models of synaptic connections, commonly referred to as memristors. These are electrically rewritable nanoscale components of non-volatile memory [1–3]. By using these components, it is feasible to significantly enhance the effectiveness and energy efficiency of algorithm accelerators which are based on ANNs [4–6]. Additionally, it enables the development of promising computing systems relying on bioplausible third-generation neural network algorithms, namely Spiking Neural Networks (SNNs) [7–9].

The paper discusses the original approach to establishing optimal rules for tuning local SNNs with frequency encoding. It also explores the potential implementation of said rules using Spike-Timing-Dependent Plasticity (STDP) [10]. The study demonstrates the stability of SNN learning when subjected to analogue memristors’ variance, and it highlights noise as an effective tool for fine-tuning and sustaining SNN memristive weights [7, 11].

Approaches to implementing local plasticity rules with dopamine-like modulation are discussed as a type of SNN reinforcement learning. This approach is necessary for forming imitative “needs” of an agent during autonomous functioning [12–14]. In addition, the first results of the creation of a prototype of a memristive implantable neuroprosthesis for motor activity are examined [15, 16].

Finally, potential hardware solutions for both neuronal components and synaptic connections using suitable memristive devices are demonstrated. The concept and initial findings of an analog neuromorphic computing system created with the aforementioned components are presented.

Thus, this paper aims to organize current and novel approaches for implementing energy-efficient and compact analog neuromorphic computing systems that can enable real-time processing and lifelong learning in artificial intelligence.

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 was carried out with the financial support of the Ministry of Science and Higher Education, agreement No. 075-15-2023-324.

Competing interests. The authors declare that they have no competing interests.

参考

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Martyshov MN, Emelyanov AV, Demin VA, et al. Multifilamentary character of anticorrelated capacitive and resistive switching in memristive structures based on (Co-Fe-B)x(LiNbO3)100−x nanocomposite. Phys Rev Applied. 2020;14:034016.
Minnekhanov AA, Emelyanov AV, Lapkin DA, et al. Parylene-based memristive devices with multilevel resistive switching for neuromorphic applications. Sci Rep. 2019;9(1):10800. doi: 10.1038/s41598-019-47263-9
Prezioso M, Merrikh-Bayat F, Hoskins BD, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature. 2015;521(7550):61–64. doi: 10.1038/nature14441
Emelyanov AV, Lapkin DA, Demin VA, Erokhin VV. First steps towards the realization of a double layer perceptron based on organic memristive devices. AIP Advances. 2016;6(11):111301. doi: 10.1063/1.4966257
Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature. 2020;577(7792):641–646. doi: 10.1038/s41586-020-1942-4
Emelyanov AV, Nikiruy KE, Serenko AV, et al. Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights. Nanotechnology. 2020;31(4):045201. doi: 10.1088/1361-6528/ab4a6d
Wang W, Pedretti G, Milo V, et al. Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses. Sci Adv. 2018;4(9):eaat4752. doi: 10.1126/sciadv.aat4752
Gerasimova SA, Mikhaylov AN, Belov AI, et al. Design of memristive interface between electronic neurons. AIP Conf Proc. 2018;1959(1):090005. doi: 10.1063/1.5034744
Demin VA, Nekhaev DV, Surazhevsky IA, et al. Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Netw. 2021;134:64–75. doi: 10.1016/j.neunet.2020.11.005
Surazhevsky IA, Demin VA, Ilyasov AI, et al. Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network. Chaos, Solitons and Fractals. 2021;146:110890. doi: 10.1016/j.chaos.2021.110890
Nikiruy KE, Emelyanov AV, Demin VA, et al. Dopamine-like STDP modulation in nanocomposite memristor. AIP Advances. 2019;9(6):065116. doi: 10.1063/1.5111083
Minnekhanov AA, Shvetsov BS, Emelyanov AV. Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning. J Phys D: Appl Phys. 2021;54(48):484002. doi: 10.1088/1361-6463/ac203c
Vlasov D, Rybka R, Sboev A. Reinforcement learning in a spiking neural network with memristive plasticity. In: Reinforcement learning in a spiking neural network with memristive plasticity. 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA); 2022; Kaliningrad, Russian Federation. P. 300–302. doi: 10.1109/DCNA56428.2022.9923314
Mikhaylov A, Pimashkin A, Pigareva Y, et al. Neurohybrid memristive CMOS-integrated systems for biosensors and neuroprosthetics. Front Neurosci. 2020;14:358. doi: 10.3389/fnins.2020.00358
Masaev DN, Suleimanova AA, Prudnikov NV, et al. Memristive circuit-based model of central pattern generator to reproduce spinal neuronal activity in walking pattern. Front Neurosci. 2023;17:1124950. doi: 10.3389/fnins.2023.1124950

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Principles of analog neuromorphic computing: from components to systems and algorithms

全文:

详细

关键词

全文:

ADDITIONAL INFORMATION

作者简介

V. Demin

A. Emelyanov

K. Nikiruy

I. Surazhevsky

A. Sitnikov

V. Rylkov

P. Kashkarov

M. Kovalchuk

参考

补充文件