FPGA-Efficient Digital Implementation of a Multiplierless Cochlea–Neuron Interaction Model for Industrial-Scale Neural Systems

This paper presents a novel digital design methodology for modeling cochlea-neuron interactions, tailored for applications within the scope of industrial electronics, such as real-time biosensing, smart health interfaces, and resource-aware neural signal processing. The proposed model employs a simplified two-dimensional cochlear structure based on the Hopf oscillator, optimized using Linear Shift-ADD-Based (LS-A-B) functions and Look-Up Table-Based Sampling (LUT-BS) to eliminate complex multipliers and achieve hardware-friendly, high-speed performance. This hybrid multiplierless architecture aligns with the journal's focus on efficient digital realization of intelligent systems and FPGA-based electronic designs. The resulting Cochlea-Neuron Interaction Circuit (C-NIC), when implemented on a Xilinx Virtex-II FPGA, demonstrates 1.33× speed-up and supports up to 87 parallel cochlear modules, while maintaining high signal fidelity and neural activation accuracy. Simulation and hardware validation confirm that the proposed system provides a scalable, low-resource solution suitable for emerging industrial biosensing systems, smart auditory devices, and embedded neural interfaces. The methodology contributes to advancing the real-time digital implementation of biologically-inspired systems in industrial and biomedical electronics.

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Publication Details

Journal
IEEE Open Journal of the Industrial Electronics Society
Published
2025-01-01
DOI
https://doi.org/10.1109/ojies.2025.3592720
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

FPGA-Efficient Digital Implementation of a Multiplierless Cochlea–Neuron Interaction Model for Industrial-Scale Neural Systems

Songjie Xiang, Guodao Zhang, Mohammad Sh. Daoud, Yanling Chu et al.
1 citations
IEEE Open Journal of the Industrial Electronics Society
Neural Networks and Applications
2.48
article

FPGA-Efficient Digital Implementation of a Multiplierless Cochlea–Neuron Interaction Model for Industrial-Scale Neural Systems

Songjie Xiang, Guodao Zhang, Mohammad Sh. Daoud, Yanling Chu, Die Yu, Abdulilah Mohammad Mayet, Hailing Liu, Ru Chen, Yideng Huang
article en
1 citations

Abstract

This paper presents a novel digital design methodology for modeling cochlea-neuron interactions, tailored for applications within the scope of industrial electronics, such as real-time biosensing, smart health interfaces, and resource-aware neural signal processing. The proposed model employs a simplified two-dimensional cochlear structure based on the Hopf oscillator, optimized using Linear Shift-ADD-Based (LS-A-B) functions and Look-Up Table-Based Sampling (LUT-BS) to eliminate complex multipliers and achieve hardware-friendly, high-speed performance. This hybrid multiplierless architecture aligns with the journal's focus on efficient digital realization of intelligent systems and FPGA-based electronic designs. The resulting Cochlea-Neuron Interaction Circuit (C-NIC), when implemented on a Xilinx Virtex-II FPGA, demonstrates 1.33× speed-up and supports up to 87 parallel cochlear modules, while maintaining high signal fidelity and neural activation accuracy. Simulation and hardware validation confirm that the proposed system provides a scalable, low-resource solution suitable for emerging industrial biosensing systems, smart auditory devices, and embedded neural interfaces. The methodology contributes to advancing the real-time digital implementation of biologically-inspired systems in industrial and biomedical electronics.

IEEE Open Journal of the Industrial Electronics SocietyVol. 6
Al Ain University (AE), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Quzhou City People's Hospital (CN), Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (CN), Quzhou University (CN), Hangzhou Dianzi University (CN), King Khalid University (SA)
Openalex Percentile: Top 9%
Neural Networks and Applications
2.48
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