Home // ALLDATA 2020, The Sixth International Conference on Big Data, Small Data, Linked Data and Open Data // View article
Authors:
Ilkay Wunderlich
Benjamin Koch
Sven Schönfeld
Keywords: convolutional neural network; image processing; re-configurable hardware; batchnorm fusing; pruning; quantization;
Abstract:
Convolutional Neural Networks (CNNs) have gained high popularity as a tool for computer vision tasks and for that reason are used in various applications. There are many different concepts, like single shot detectors, that have been published for detecting objects in images or video streams. However, CNNs suffer from disadvantages regarding the deployment on embedded platforms such as re-configurable hardware like Field Programmable Gate Arrays (FPGAs). Due to the high computational intensity, memory requirements and arithmetic conditions, a variety of strategies for running CNNs on FPGAs have been developed. The following methods showcase our best practice approaches for a TinyYOLOv3 detector network on a XILINX Artix-7 FPGA using techniques like fusion of batch normalization, filter pruning and post training network quantization.
Pages: 34 to 40
Copyright: Copyright (c) IARIA, 2020
Publication date: February 23, 2020
Published in: conference
ISSN: 2519-8386
ISBN: 978-1-61208-775-7
Location: Lisbon, Portugal
Dates: from February 23, 2020 to February 27, 2020