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Understanding timing impact of BTI/RTN with massively threaded atomistic transient simulations.

, , , , and . ICICDT, page 1-4. IEEE, (2014)

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Understanding timing impact of BTI/RTN with massively threaded atomistic transient simulations., , , , and . ICICDT, page 1-4. IEEE, (2014)Capturing True Workload Dependency of BTI-induced Degradation in CPU Components., , , , , , , , , and 1 other author(s). ACM Great Lakes Symposium on VLSI, page 373-376. ACM, (2016)Exploring the Design Space of Efficient Deep Neural Networks., , , , and . SEC, page 317-318. IEEE, (2020)Hardware-aware machine learning: modeling and optimization., , and . ICCAD, page 137. ACM, (2018)\emphNeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks., , , and . ACML, volume 77 of Proceedings of Machine Learning Research, page 622-637. PMLR, (2017)Can We Guarantee Performance Requirements under Workload and Process Variations?, and . ISLPED, page 308-313. ACM, (2016)Stable Diffusion For Aerial Object Detection., , , and . CoRR, (2023)Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination., , , , , , , , , and 5 other author(s). CoRR, (2020)Exploring aging deceleration in FinFET-based multi-core systems., , and . ICCAD, page 111. ACM, (2016)Designing adaptive neural networks for energy-constrained image classification., , , , , , and . ICCAD, page 23. ACM, (2018)