Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks

S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, and D. S. Modha, “Learned step size quantization,” 2019, arXiv preprint, arXiv:1902.08153

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D. Lin, S. Talathi, and S. Annapureddy, “Fixed point quantization of deep convolutional networks,” In Proceedings of International Conference on Machine Learning (ICML), 2016, pp. 2849–2858

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V. Akhlaghi, A. Yazdanbakhsh, K. Samadi, R. K. Gupta, and Hadi Esmaeilzadeh, “Snapea: Predictive early activation for reducing computation in deep convolutional neural networks,” In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), 2018, pp. 662–673

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L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model compression and hardware acceleration for neural networks: A comprehensive survey,” In Proceedings of the IEEE 108, no. 4, 2020, pp. 485–532

2020

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S. Gupta, S. Ullah, K. Ahuja, A. Tiwari, and A. Kumar, “ALigN: A Highly Accurate Adaptive Layerwise Log2Lead Quantization of Pre-Trained Neural Networks,” In IEEE Access, vol. 8, 2020, pp. 118899-118911

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M. Nagel, R. A. Amjad, M. Van Baalen, C. Louizos, and T. Blankevoort, “Up or Down? Adaptive Rounding for Post-Training Quantization,” In Proceedings of International Conference on Machine Learning (ICML), 2020, pp. 7197–7206

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T. Stadtmann, C. Latotzke, and T. Gemmeke, “From Quantitative Analysis to Synthesis of Efficient Binary Neural Networks,” In Proceedings of the 19th IEEE International Conference On Machine Learning And Applications (ICMLA), 2020, pp. 93–100

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