Curra-Sosa, Dagnier A.Tapiador Morales, RicardoGómez Rodríguez, Francisco de AsísLinares Barranco, Alejandro2024-10-252024-10-252024-08978-3-031-64105-3978-3-031-64106-0https://hdl.handle.net/11441/164144Part of the book series: Springer Proceedings in Materials ((SPM,volume 50)) Included in the following conference series: X Workshop in R&D+i & International Workshop on STEM of EPSA promising alternative in artificial vision tasks that considerably re-duces computational cost and power is neuromorphic event-based processing. In this context, we employed a multiconvolution event-based system on a FPGA, inspired by the Leaky Integrate-and-Fire (LIF) neuron, to demonstrate its viabil-ity in implementing a Spiking Convolutional Neural Network (SCNN). In this work, we show that the convolution layers of the LeNet-5 network, trained with MNIST, can be implemented in a spiking manner, and discuss the necessary mod-ifications to the architecture to offer this solution in real-time.application/pdf11 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Computational VisionConvolutional Neural NetworksDynamic Vision SensorNeuromorphic EngineeringFPGAEvent-based representationinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-031-64106-0_43