Spiking neural network analog two-variable neuron implementation
Year: 2026
Type: article
Abstract: Currently, the problem of using neural networks in embedded systems, which have constraints on power consumption and area occupied, is relevant. At the same time, there is growing interest in spiking neural networks – more biologically realistic neural networks with potentially higher energy efficiency. This paper presents a review of existing hardware implementations of spiking neural networks, implemented both in the digital and analog domains. A hardware implementation of analog spiking neuron and synapse fabricated in Mikron JSC 180 nm CMOS technology with a reduced supply voltage of 0.6 V is presented. According to the simulation results, the energy consumption of a single neuron was 1.46 pJ/spike. The paper also provides a formula for comparing energy consumption between neurons fabricated in different technologies and with different supply voltages. According to this formula, the energy consumption of the implemented neuron is comparable to that of the referenced sources.
Authors: Skrebenkov, Danil, Budanov, Dmitry
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7170829004
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