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Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

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Bit-Pragmatic Deep Neural Network Computing., , , , and . ICLR (Workshop), OpenReview.net, (2017)Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation, , , , and . (2020)cite arxiv:2004.09602Comment: 20 pages, 7 figures.Bit-pragmatic Deep Neural Network Computing., , , , and . CoRR, (2016)Value-Based Deep-Learning Acceleration., , , , , , , and . IEEE Micro, 38 (1): 41-55 (2018)Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing., , , , , and . ISCA, page 1-13. IEEE Computer Society, (2016)Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How, , , , , , , and . (2018)cite arxiv:1803.03688Comment: An earlier version of this work titled "JaZ: Enabling Innovation Towards Chaff-Free Deep Learning Computing" was submitted for blind review.FP8 Formats for Deep Learning., , , , , , , , , and 5 other author(s). CoRR, (2022)Loom: exploiting weight and activation precisions to accelerate convolutional neural networks., , , , and . DAC, page 20:1-20:6. ACM, (2018)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Evaluating the memory system behavior of smartphone workloads., , , , , , , , , and 1 other author(s). ICSAMOS, page 83-92. IEEE, (2014)