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Neural Network-based Vehicle Image Classification for IoT Devices

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationThe IEEE International Workshop on Signal Processing Systems
PublisherIEEE
Pages148-153
Number of pages6
ISBN (Electronic)978-1-7281-1927-4
DOIs
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventIEEE International Workshop on Signal Processing Systems -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Workshop on Signal Processing Systems
ISSN (Electronic)2374-7390

Conference

ConferenceIEEE International Workshop on Signal Processing Systems
Period1/01/00 → …

Abstract

Convolutional Neural Networks (CNNs) have previously provided unforeseen results in automatic image analysis and interpretation, an area which has numerous applications in both consumer electronics and industry. However, the signal processing related to CNNs is computationally very demanding, which has prohibited their use in the smallest embedded computing platforms, to which many Internet of Things (IoT) devices belong. Fortunately, in the recent years researchers have developed many approaches for optimizing the performance and for shrinking the memory footprint of CNNs. This paper presents a neural-network-based image classifier that has been trained to classify vehicle images into four different classes. The neural network is optimized by a technique called binarization, and the resulting binarized network is placed to an IoT-class processor core for execution. Binarization reduces the memory footprint of the CNN by around 95% and increases performance by more than 6×. Furthermore, we show that by utilizing a custom instruction ’popcount’ of the processor, the performance of the binarized vehicle classifier can still be increased by more than 2×, making the CNN-based image classifier suitable for the smallest embedded processors.

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