TY - GEN
T1 - FruitVegCNN
T2 - Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC
AU - Dey, Somdip
AU - Saha, Suman
AU - Singh, Amit
AU - Mcdonald-Maier, Klaus D.
PY - 2020/7/23
Y1 - 2020/7/23
N2 - Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become apopular application in the agricultural industry, however, to the best of our knowledge no pre-viously recorded study has designed and evaluated such an application on a mobile platform. Inthis paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification offruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluatedthe efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat-forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacyand power efficiency of our proposed CNN architecture
AB - Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become apopular application in the agricultural industry, however, to the best of our knowledge no pre-viously recorded study has designed and evaluated such an application on a mobile platform. Inthis paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification offruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluatedthe efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat-forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacyand power efficiency of our proposed CNN architecture
UR - https://doi.org/10.36227/techrxiv.12686051
U2 - 10.36227/techrxiv.12686051
DO - 10.36227/techrxiv.12686051
M3 - Other contribution
ER -