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Post-training Piecewise Linear Quantization for Deep Neural Networks.

, , , , , and . ECCV (2), volume 12347 of Lecture Notes in Computer Science, page 69-86. Springer, (2020)

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Learning and reachability analysis for stochastic hybrid systems using mixtures of Gaussian processes., and . MED, page 332-337. IEEE, (2016)Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation., , , , , and . CoRR, (2020)Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators., , , , and . MLSys, mlsys.org, (2021)Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC., , , , , , , , , and 13 other author(s). ISCA, page 15-28. IEEE, (2021)A Data-Driven Approach for Modeling, Analysis and Control of Stochastic Hybrid Systems using Gaussian Processes.. Vanderbilt University, Nashville, Tennessee, USA, (2018)base-search.net (ftvanderbilt:oai:ir.vanderbilt.edu:1803/10418).Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes., and . J. Control. Sci. Eng., (2017)Post-training Piecewise Linear Quantization for Deep Neural Networks., , , , , and . ECCV (2), volume 12347 of Lecture Notes in Computer Science, page 69-86. Springer, (2020)Griffin: Rethinking Sparse Optimization for Deep Learning Architectures., , , , , and . HPCA, page 861-875. IEEE, (2022)Design Space Exploration of Sparse Accelerators for Deep Neural Networks., , , , , and . CoRR, (2021)Simulation based evaluation of security and resilience in railway infrastructure., , , and . HotSoS, page 22:1-22:2. ACM, (2019)