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FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference.

, , , , and . DAC, page 200. ACM, (2019)

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ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection., , , and . CoRR, (2019)Regularizing Activation Distribution for Training Binarized Deep Networks., , , and . CVPR, page 11408-11417. Computer Vision Foundation / IEEE, (2019)AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling., , and . MLSys, mlsys.org, (2019)QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration., , , , , , and . CoRR, (2022)IPSA: Integer Programming via Sparse Approximation for Efficient Test-Chip Design., , , , and . ICCD, page 11-19. IEEE, (2019)Towards Efficient Model Compression via Learned Global Ranking., , , and . CVPR, page 1515-1525. Computer Vision Foundation / IEEE, (2020)Enhancing precipitation models by capturing multivariate and multiscale climate dynamics., , , , and . CySWATER@CPSWeek, page 39-42. ACM, (2017)Quantized deep neural networks for energy efficient hardware-based inference., , , and . ASP-DAC, page 1-8. IEEE, (2018)ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection., , , and . WACV, page 1169-1178. IEEE, (2020)Single-Path NAS: Designing Hardware-Efficient ConvNets in Less Than 4 Hours., , , , , , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 481-497. Springer, (2019)