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Measuring scheduling efficiency of RNNs for NLP applications., , , and . CoRR, (2019)Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications., , and . SysML, mlsys.org, (2019)Pushing the limits of RNN Compression., , , , , , and . EMC2@NeurIPS, page 18-21. IEEE, (2019)Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products., , , , , , and . ACM J. Emerg. Technol. Comput. Syst., 17 (4): 46:1-46:18 (2021)Rank and run-time aware compression of NLP Applications., , , , and . SustaiNLP@EMNLP, page 8-18. Association for Computational Linguistics, (2020)Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks., , , , , , , and . FPGA, page 16-25. ACM, (2016)Skipping RNN State Updates without Retraining the Original Model., , , and . SenSys-ML, page 31-36. ACM, (2019)Run-Time Efficient RNN Compression for Inference on Edge Devices., , , , and . EMC2@HPCA/CVPR/ISCA, page 26-30. IEEE, (2019)Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs., , , , , and . EMC2@HPCA/CVPR/ISCA, page 1-5. IEEE, (2019)A power-efficient 32b ARM ISA processor using timing-error detection and correction for transient-error tolerance and adaptation to PVT variation., , , , , and . ISSCC, page 284-285. IEEE, (2010)