NEUROMORPHIC PROCESSORS: DESIGN PRINCIPLES AND COMPARATIVE REVIEW
DOI:
https://doi.org/10.17721/AIT.2025.08Keywords:
neuromorphic processor, neuromorphic architecture, spiking neural network.Abstract
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Background. The paper addresses the scientific and practical problem of designing neuromorphic processors and systems based on spiking neural networks (SNNs). Neuromorphic processors constitute a distinct class of computing systems whose operation is inspired by the biological and physical principles of the human brain. Unlike traditional von Neumann architectures, these processors integrate memory and computation, enabling massive parallelism and energy-efficient execution of complex cognitive tasks. Their hardware architecture is fundamentally based on neurons and synapses rather than transistors.
Methods. The study applies analytical and comparative methods to examine neuromorphic computing systems. Spiking neuron models are comparatively analyzed with respect to their application domains, with detailed consideration of the Integrate-and-Fire model and its mathematical formulation. Learning methods for SNNs are also evaluated, focusing on weight update mechanisms, energy efficiency, and computational complexity. Special attention is given to Spike-Timing-Dependent Plasticity (STDP). In addition, the deployment of SNNs on neuromorphic chips is analyzed, including core partitioning, routing optimization, weight storage, and learning support.
Results. The analysis identifies the advantages and limitations of spiking neuron models and learning methods for neuromorphic hardware. The Integrate-and-Fire model is shown to be the most practical engineering solution due to its simplicity and biological relevance. STDP is confirmed as an effective learning mechanism that supports adaptive behavior and energy efficiency. Architectural design choices are shown to be critical for scalable and efficient system deployment.
Conclusions. The paper presents a comparative overview of modern neuromorphic processors, emphasizing architecture, energy efficiency, and application areas. The results demonstrate the potential of neuromorphic computing for implementing SNNs and solving complex cognitive tasks. The article serves as a concise review and reference for further research in neuromorphic engineering.
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Copyright (c) 2026 Катерина ЯЛОВА, канд. техн. наук, доц., Михайло БАБЕНКО, канд. техн. наук, доц. (Автор)
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