[1] J. Lin, W.-M. Chen, Y. Lin, C. Gan, and S. Han, "Mcunet: Tiny deep learning on iot devices," Advances in Neural Information Processing Systems, vol. 33, 2020.
[2] J. Lin, W.-M. Chen, H. Cai, C. Gan, and S. Han, "Mcunetv2: Memory-efficient patch-based inference for tiny deep learning," in Annual Conference on Neural Information Processing Systems (NeuralPS), 2021.
[3] R. David, J. Duke, A. Jain, V. Janapa Reddi, N. Jeffries, J. Li, N. Kreeger, I. Nappier, M. Natraj, T. Wang, et al., "Tensorflow lite micro: Embedded machine learning for tinyml systems," Proceedings of Machine Learning and Systems (MLSys), vol. 3, pp. 800-811, 2021.
[4] T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, M. Cowan, H. Shen, L. Wang, Y. Hu, L. Ceze, et al., "Tvm: An automated end-to-end optimizing compiler for deep learning," arXiv preprint arXiv:1802.04799, 2018.
[5] Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Gan, Chuang and Han, Song, "MCUNet Model Zoo." [Online]. Available: https://github.com/mit-han-lab/mcunet/blob/master/ Mcunet/model_zoo.py, 2023. (accessed May. 16, 2023).
[6] L. Lai, N. Suda, and V. Chandra, "Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus," arXiv preprint arXiv:1801.06601, 2018.
[7] ARM Ltd., "Arm Mbed CLI." [Online]. Available: https://github.com/ARMmbed/mbed-cli, 2023. (accessed May. 16, 2023).
[8] STI Ltd., "STM32F767ZI Datasheet." [Online]. Available: https://www.st.com/en/microcontrollers-microprocessors stm32f767zi.html, 2023. (accessed May. 16, 2023).
[9] Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, "Amc: Automl for model compression and acceleration on mobile devices," in Proceedings of the European conference on computer vision (ECCV), pp. 784-800, 2018.
[10] S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “Eie: Efficient inference engine on compressed deep neural network,” in Proceedings of the International Symposium on Computer Architecture (ISCA), p. 243–254, 2016.
[11] S. Han, H. Mao, and W. J. Dally, "Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding," arXiv preprint arXiv:1510.00149, 2015.
[12] J. Lin, Y. Rao, J. Lu, and J. Zhou, “Runtime neural pruning,” Advances in neural information processing systems, vol. 30, 2017.
[13] Y. He, X. Zhang, and J. Sun, "Channel pruning for accelerating very deep neural networks," in Proceedings of the IEEE international conference on computer vision, pp. 1389-1397, 2017.
[14] Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang, "Learning efficient convolutional networks through network slimming," in Proceedings of the IEEE international conference on computer vision, pp. 2736-2744, 2017.
[15] Z. Liu, H. Mu, X. Zhang, Z. Guo, X. Yang, K.-T. Cheng, and J. Sun, “Metapruning: Meta learning for automatic neural network channel pruning,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 3296–3305, 2019.
[16] H. F. Langroudi, V. Karia, T. Pandit, and D. Kudithipudi, “Tent: Efficient quantization of neural networks on the tiny edge with tapered fixed point,” arXiv preprint arXiv:2104.02233, 2021.
[17] M. Rusci, A. Capotondi, and L. Benini, "Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers," Proceedings of Machine Learning and Systems, vol. 2, pp. 326-335, 2020.