@inproceedings{a9294a64b8b54a7fb03fdcf1ad7ccdcf,
title = "Deep Convolutional Network: an Event-based approach",
abstract = "In this paper, we propose a methodology to incrementally compute a convolutional layer of a neural network based on events, and an architecture that can efficiently implement it. In order to illustrate the approach, we present an application example, where we train a traditional DNN based on a LeNet architecture using a traffic signs dataset and then, we replace the first convolutional layer with our event based approach.",
keywords = "Image resolution, Image recognition, Neural networks, Memory management, Standards, CNN, event-based imager, Deep Neural network",
author = "Ivanovich, {D. Gigena} and N. Rodr{\'i}guez and A. Pasciaroni and P. Juli{\'a}n",
year = "2021",
month = mar,
day = "12",
doi = "10.1109/CAE51562.2021.9397561",
language = "English",
isbn = "9781728175799",
series = "2021 Argentine Conference on Electronics - Congreso Argentino de Electronica 2021, CAE 2021",
publisher = "IEEE Computer Society",
pages = "50--54",
booktitle = "2021 Argentine Conference on Electronics - Congreso Argentino de Electronica 2021, CAE 2021",
address = "United States",
note = "2021 Argentine Conference on Electronics (CAE) ; Conference date: 11-03-2021 Through 12-03-2021",
}