References
- S. Lin, Y. Zhang, C. Hong, M. Skach, M. Haque L. Tang and J. Mars, " The Architectural Implications of Autonomous Driving: Constraints and Acceleration," in Proceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems, Williamsburg, USA, pp. 751-66, 2018.
- J. Dyrstad and J. Mathiassen, "Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality," in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China, 2017.
- D. Vasisht. Z. Kapetanovic, J. Won, X. Jin, R. Chandra, A. Kapoor, N. sinha, and M. Sudarshan, "FarmBeats: An IoT Platform for Data-Driven Agriculture," in Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, Boston, USA, 2017.
- T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam, "DianNao: A small-footprint high-throughput accelerator for ubiquitous machine-learning," in Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, Salt Lake, Utah, pp. 269-284, 2014.
- V. Sze, Y. Chen, T. Yang, and J. S. Emer, "Efficient Processing of Deep Neural Networks: A Tutorial and Survey," in Proceedings of the IEEE, vol. 105, no. 12, pp. 2295-2329, Jan. 2017. https://doi.org/10.1109/JPROC.2017.2761740
- Jetson AGX Xavier Developer Kit [Internet]. Available: https://developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proceedings of the 26th Conference on Neural Information Processing Systems, Lake Tahoe, pp. 1097-1105, 2012.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proceedings of the International Conference on Learning Representations, San Diego, CA, 2015.
- H. Kim, J. Kim, and H. Jung, "Convolutional Neural Network Based Image Processing System," Journal of Information and Communication Convergence Engineering, vol. 16, no. 3, pp. 160-165, Sep. 2018. https://doi.org/10.6109/JICCE.2018.16.3.160
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, pp. 770-778, 2016.
- Caffe, Deep learning framework by BAIR [Internet]. Available: http://caffe.berkeleyvision.org/.
- Torch, [Internet]. Available: http://torch.ch/.
- TensorFlow, [Internet]. Available: http://download.tensorflow.org/paper/whitepaper2015.pdf/.
- S. Huh, J. Yoo, M. Kim and S. Hong, "Providing Fair Share Scheduling on Multicore Cloud Servers via Virtual Runtime-based Task Migration Algorithm", in Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems (ICDCS), Macau, China pp. 606-614, 2012.
- S. Eyerman and L. Eeckhout,"System-Level Performance Metrics for Multiprogram Workloads" in Micro, IEEE. vol. 28, pp. 42-53, 2008.
- L. Nguyen, D. Lin, Z. Lin and J. Cao, "Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation", in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 2018.
- X. Yu, N. Zeng, S. Liu and Y. Zhang, "Utilization of DenseNet201 for diagnosis of breast abnormality", in Machine Vision and Applications. vol. 30, Oct. 2019.