Abstract
Bluebox 2.0 by NXP Semiconductors, which has goal of enabling autonomy in vehicles for ADAS applications, is used to enhance car capabilities to perform sensor fusion and run AI algorithms. It focuses on sensor data coming from radars, lidars, and cameras. This research focuses on enabling computer vision application, Image Classification, by implementation of Convolutional Neural Networks in Bluebox 2.0. In this paper, two CNN architectures namely A-MnasNet and R-MnasNet have implemented on Bluebox 2.0. These models have been derived by Design Space Exploration of MnasNet, a CNN architecture, proposed by Google Brain team in 2019. These models have been trained and tested on CIFAR-10 dataset. The model size and accuracy of A-MnasNet are 11.6 MB and 96.89% and that of R-MnasNet are 3 MB and 91.13% respectively. They outperform the MnasNet architecture which has an accuracy of 80.8% and a model size of 12.7 MB. These neural networks can also be used to perform other computer vision applications.
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Shah, P., El-Sharkawy, M.: A-MnasNet: augmented MnasNet for computer vision. In: IEEE 63rd International Midwest Symposium on Circuits & Systems (MWSCAS 2020), Springfield, Massachusetts, 9–12 August 2020
Shah, P., El-Sharkawy, M.: R-MnasNet: reduced MnasNet for computer vision. In: International IOT, Electronics and Mechatronics Conference (IEMTRONICS 2020), Vancouver, Canada, 9–12 September 2020
Tan, M., Chen, B., et al.: MnasNet: Platform-Aware Neural Architecture Search for Mobile, arXiv:1807.11626v3 [cs.CV], 29 May 2019. Accessed 14 Apr 2021
Li, D., Zhou, A., Yao, A.: HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions, arXiv:1908.03888v1 [cs.CV], 11 August 2019. Accessed 14 Apr 2021
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: Learning Augmentation Strategies from Data, arXiv:1805.09501v3 (2019). Accessed 14 Apr 2021
https://www.cs.toronto.edu/kriz/cifar.html. Accessed 14 Apr 2021
Misra, D.: Mish: A Self Regularized Non-Monotonic Neural Activation Function, arXiv preprint arxiv:1908.08681 (2019). Accessed 14 Apr 2021
Ruder, S.: An overview of gradient descent optimization algorithms, arXiv Preprint arXiv:1609.04747 (2016). Accessed 14 Apr 2021
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Shah, P., El-Sharkawy, M. (2021). Image Classification with A-MnasNet and R-MnasNet on NXP Bluebox 2.0. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_55
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DOI: https://doi.org/10.1007/978-3-030-80126-7_55
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