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Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado

Maruf Ahmad1, Lei Zhang1, Kelvin Tsun Wai Ng1

  • 1Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.

Biomimetics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary

Complex-exponential neurons on FPGAs offer a solution to power and delay issues in artificial neural networks. Vivado BRAM implementations provide efficient hardware for bio-inspired spiking neural networks.

Keywords:
FPGA implementationcomplex exponential neuronneural encodingspiking neural networks

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Neuroscience

Background:

  • Conventional artificial neural networks face computational constraints, leading to high power consumption and delays, especially with big data.
  • Complex-exponential-based neurons offer a computationally efficient alternative by simplifying calculations.
  • Spiking neural networks (SNNs) are bio-inspired models that can potentially overcome these limitations.

Purpose of the Study:

  • To investigate the hardware implementation of complex-exponential-based neurons on Field-Programmable Gate Arrays (FPGAs).
  • To evaluate the performance, accuracy, and resource efficiency of these implementations.
  • To demonstrate a pathway for developing efficient bio-inspired SNNs.

Main Methods:

  • Implementation of two-neuron and multi-neuron models using Xilinx System Generator and Vivado Design Suite.
  • Utilizing 8-bit, 16-bit, and 32-bit fixed-point data formats.
  • Evaluation of accuracy, operating frequency, power consumption, and resource utilization (LUTs, FFs, BRAMs).

Main Results:

  • Vivado BRAM-based designs demonstrated superior speed, power efficiency, and resource utilization compared to Simulink.
  • The proposed neuron model achieved high accuracy across different FPGA implementations.
  • The Vivado BRAM approach successfully supported up to 128 neurons with optimal resource usage.

Conclusions:

  • Complex-exponential neurons are a viable and efficient approach for FPGA implementation of SNNs.
  • Vivado BRAM-based designs offer significant advantages for hardware acceleration of SNNs.
  • This research provides a foundation for designing energy-efficient and high-performance bio-inspired computing systems.