Data Sampling System for Processing Event Camera Data Using a Stochastic Neural Network on an FPGA
Document Type
Article
Date of Original Version
8-1-2025
Abstract
The use of a stochastic artificial neural network (SANN) implemented on a Field Programmable Gate Array (FPGA) provides a promising method of performing image recognition on event camera recordings, however, challenges exist due to the fact that event camera data has an inherent unevenness in the timing at which data is sent out of the camera. This paper proposes a sampling system to overcome this challenge, by which all “events” occurring at specific timestamps in an event camera recording are selected (sampled) to be processed and sent to the SANN at regular intervals. This system is implemented on an FPGA in SystemVerilog, and to test it, simulated event camera data is sent to the system from a computer running MATLAB (version 2022+). The sampling system is shown to be functional. Analysis is shown demonstrating its performance regarding data sparsity, time convergence, normalization, repeatability, range, and some characteristics of the hold system.
Publication Title, e.g., Journal
Electronics Switzerland
Volume
14
Issue
15
Citation/Publisher Attribution
Shively, Seth, Nathaniel Jackson, Eugene Chabot, John DiCecco, and Scott Koziol. "Data Sampling System for Processing Event Camera Data Using a Stochastic Neural Network on an FPGA." Electronics Switzerland 14, 15 (2025). doi: 10.3390/electronics14153094.