Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.082979
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

NeuroPulse: Spiking-Transformer Hybrid Architecture for Ultra-Low-Power Continual Learning in Neuromorphic Network Processors

Mohammed Abdullah Alsuwaiket*
Department of Computer Science, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia
* Corresponding Author: Mohammed Abdullah Alsuwaiket. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082979

Received 26 March 2026; Accepted 09 May 2026; Published online 29 June 2026

Abstract

Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a Hebbian Synaptic Consolidation (HSC) mechanism, eliminating the need for full model retraining. Experiments on NSL-KDD, UNSW-NB15, and real-world 5G RAN telemetry datasets demonstrate that NeuroPulse achieves 94.3% intrusion detection accuracy at 0.23 mW average energy consumption—a 12× power reduction over transformer-only baselines—while retaining 97.1% of accumulated knowledge after 50 sequential task updates, making it uniquely suited for always-on intelligent network nodes.

Keywords

Spiking neural networks; neuromorphic computing; sparse self-attention; continuous learning; intrusion detection; energy efficient AI; network processors

  • 15

    View

  • 3

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link