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FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC
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  • Somdip Dey ,
  • Suman Saha ,
  • Amit Singh ,
  • Klaus D. Mcdonald-Maier
Somdip Dey
University of Essex

Corresponding Author:[email protected]

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Suman Saha
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Amit Singh
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Klaus D. Mcdonald-Maier
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Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the 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 efficacy and power efficiency of our proposed CNN architecture.