참고문헌
- ETRI Technology, Aldebaran microcontroller SoC for mobile robot (low power MCU core technology), 2017, available at https://www.etri.re.kr/eng/bbs/view.etri?b_board_id=ENG03&b_idx=16719
- J. Han et al., A 1GHz fault tolerant processor with dynamic lockstep and self-recovering cache for ADAS SoC complying with ISO26262 in automotive electronics, in Proc. IEEE Asian Solid-State Circuits Conf. (Seoul, Rep. of Korea), Nov. 2017, pp. 313-316.
- Y. Jia, Learning semantic image representations at a large scale, Ph.D. Thesis, EECS Department, Univ. of California, Berkeley, May 2014.
- S. Gupta et al., Deep learning with limited numerical precision, Int. Conf. Mach. Learn. 37 (2015), 1737-1746.
- J. Redmon and A. Farhadi, Yolo9000: Better, faster, stronger, 2016, available at https://arxiv.org/abs/1612.08242, preprint.
- J. Kim, J. K. Lee, and K. M. Lee, Accurate image super-resolution using very deep convolutional networks, in Proc. IEEE Conf. Comput. Vision Pattern Recognit. (Las Vegas, NV, USA), 2016, pp. 1646-1654.
- A. Ignatov et al., AI benchmark: All about deep learning on smartphones in 2019, in Proc. IEEE/CVF Int. Conf. Comput. Vision Workshop (Seoul, Rep. of Korea), Oct. 2019, pp. 3617-3635.
- AI-Benchmark, available at http://www.ai-bench mark.com
- J. Johnson. Benchmarks for popular CNN models, available at https://github.com/jcjoh nson/cnn-bench marks
- Coral, Edge TPU performance benchmarks, available at https://coral.ai/docs/edget pu/benchmarks/
- T. Narayan and Intel AI Academy, A comparison of performance of deep learning models on Edge using Intel Movidius Neural Compute Stick and Raspberry PI3, available at https://medium.com/intel-student-ambassadors/object-detection-a-comparison-of-performance-of-deep-learning-models-on-edge-using-intel-f66eb7f45b17
- S. Hossain and D. Lee, Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices, Sensors 19 (2019), no. 15, 3371:1-3424.
- J. Guerreiro et al., Modeling and decoupling the GPU power consumption for cross-domain DVFS, IEEE Trans. Parallel Distrib. Syst. 30 (2019), no. 11, 2494-2506. https://doi.org/10.1109/TPDS.2019.2917181
피인용 문헌
- 인공지능 프로세서 컴파일러 개발 동향 vol.36, pp.2, 2020, https://doi.org/10.22648/etri.2021.j.360204