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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Characterization of a Spiking Convolutional Processor for FPGA
Dagnier A Curra-Sosa1, Francisco Gomez-Rodriguez1, Alejandro Linares-Barranco1
1Neuromorphic Engineering Group of SCORE Excellence Unit (I3US), Department of Computer Architecture and Technology, EPS-ETSII, Universidad de Sevilla, 41004 Sevilla, Spain.
Sensors (Basel, Switzerland)
|March 28, 2026
Summary
This study evaluates the leaky-integrate and fire (LIF) neuron model for convolutional neural networks in event-based neuromorphic processing. Results show similar spike generation and distribution between software and hardware implementations for computer vision tasks.
Area of Science:
- Neuromorphic Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Event-based processing offers efficient alternatives for computer vision, optimizing computational and energy resources.
- Real-time systems require precise visual information representation from sensors.
- Machine learning models are adapted for deployment on resource-constrained hardware.
Purpose of the Study:
- To evaluate the performance limits of the leaky-integrate and fire (LIF) neuron model within convolutional layers of neural networks.
- To detail the implementation of the LIF neuron model in a hardware design with configurable parameters.
Main Methods:
- Summarized characteristics of the LIF neuron model.
- Implemented the LIF neuron model in a hardware design.
- Compared two convolution approaches: Matlab software and a Field-Programmable Gate Array (FPGA) spiking convolutional processor.
- Utilized the MNIST-DVS dataset and Sobel kernels for edge detection.
Main Results:
- The number of spikes generated by both software and hardware approaches was found to be very similar.
- The distribution of spikes by frame addresses showed a direct proportionality between the two approaches.
Conclusions:
- The LIF neuron model demonstrates comparable performance in both software and hardware implementations for event-based convolutional processing.
- This validates the potential of LIF neuron models for efficient computer vision in neuromorphic systems.
Keywords:
Address-Event-Representation (AER)DVSFPGALIF neuron modelSpiking Convolution Neural Network (SCNN)More Related Videos
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