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Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs.

, , and . Proc. ACM Program. Lang., 7 (OOPSLA2): 1648-1676 (October 2023)

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Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4505-4515. PMLR, (2019)BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models., , , , , , , and . IISWC, page 167-177. IEEE, (2019)Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression., , , , and . CoRR, (2022)The Effect of Data Dimensionality on Neural Network Prunability., , , , and . CoRR, (2022)CoMEt: x86 Cost Model Explanation Framework., , , and . CoRR, (2023)Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs., , and . CoRR, (2023)Comparing Rewinding and Fine-tuning in Neural Network Pruning., , and . ICLR, OpenReview.net, (2020)TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning., , , , , , , and . CoRR, (2020)DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates., , , and . MICRO, page 442-455. IEEE, (2020)Programming with neural surrogates of programs., , and . Onward!, page 18-38. ACM, (2021)