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Pushing the limits of RNN Compression., , , , , , and . EMC2@NeurIPS, page 18-21. IEEE, (2019)Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices., , , , and . MLSys, mlsys.org, (2021)Designing Configurable, Modifiable and Reusable Components for Simulation of Multicore Systems., , , and . SC Companion, page 472-476. IEEE Computer Society, (2012)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)Spectral prefetcher: An effective mechanism for L2 cache prefetching., , and . ACM Trans. Archit. Code Optim., 2 (4): 423-450 (2005)Extrapolation Pitfalls When Evaluating Limited Endurance Memory., , , and . MASCOTS, page 261-268. IEEE Computer Society, (2012)Measuring scheduling efficiency of RNNs for NLP applications., , , and . CoRR, (2019)Energy efficient Phase Change Memory based main memory for future high performance systems., , , and . IGCC, page 1-8. IEEE Computer Society, (2011)Manager-client pairing: a framework for implementing coherence hierarchies., , and . MICRO, page 226-236. ACM, (2011)Skipping RNN State Updates without Retraining the Original Model., , , and . SenSys-ML, page 31-36. ACM, (2019)