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Retraining-Based Iterative Weight Quantization for Deep Neural Networks, and . (2018)cite arxiv:1805.11233Comment: 12 pages, 13 figures, NIPS 2018 (32nd Annual Conference on Neural Information Processing Systems) submission.DeepTwist: Learning Model Compression via Occasional Weight Distortion., , and . CoRR, (2018)Optimal wavelength provisioning with fuzzy logic control for power saving in TWDM-PONs., , and . ICTC, page 926-929. IEEE, (2016)A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference., , , , , , , , , and 21 other author(s). VLSI Circuits, page 35-36. IEEE, (2018)Fast management of ONUs based on broadcast control channel for a 10-gigabit-capable passive optical network (XG-PON) system., , , , and . J. Commun. Networks, 15 (5): 538-542 (2013)Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization., , , , , , and . CoRR, (2021)A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics., , , , , , and . ACSCC, page 287-291. IEEE, (2021)Maximum Likelihood Training of Implicit Nonlinear Diffusion Model., , , , , and . NeurIPS, (2022)AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models., , , , , , , , , and . EMNLP (Findings), page 3288-3305. Association for Computational Linguistics, (2022)Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic., , , , , , , , , and . ICLR, OpenReview.net, (2023)