Live Demonstration: Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge

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?