Lammie, Corey, and Rahimi Azghadi, Mostafa (2020) Live demonstration: Low-Power and High-Speed Deep FPGA Inference Engines for weed classification at the edge. In: Proceedings of the 2020 IEEE International Symposium on Circuits and Systems. From: ISCAS: 2020 IEEE International Symposium on Circuits and Systems, 10-21 October 2020, Seville, Spain.

PDF (Accepted Author Version) - Accepted Version Restricted to Repository staff only

Abstract

In Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge [1] we implemented GPU- and FPGA-accelerated deterministically binarized Deep Neural Networks (DNNs), tailored toward weed species classification for robotic weed control. The dataset used consisted of 17,508 unique 256×256 color images in 9 classes, collected in situ from eight rangeland areas across Northern Australia [2]. For this live demonstration, we have designed a weed classification game. We first provide the visitor with a printed sheet showing several examples of each of the 9 various weed species classes in our dataset, to learn and memorize the weed names. This learning process can take for as long as the visitor wishes. For the game to start, five weed images from our test set are randomly selected. We then measure the interference times and accuracies for our optimized GPUand FPGA-accelerated binarized DNNs, alongside the visitors' performance. Are low-resolution, low-power, binarized DNNs able to outperform humans at categorizing weed species?

Item ID:	65713
Item Type:	Conference Item (Presentation)
ISBN:	978-1-7281-3320-1
Related URLs:	https://ieeexplore.ieee.org/abstract/doc...
Copyright Information:	(C) IEEE
Additional Information:	This was an online event.
Date Deposited:	03 Feb 2021 23:58
FoR Codes:	09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100%
Downloads:	Total: 3
	More Statistics