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Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation.

, , , , , and . HPCA, page 141-154. IEEE, (2022)

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Understanding the Future of Energy Efficiency in Multi-Module GPUs., , , and . HPCA, page 519-532. IEEE, (2019)Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties., , and . IEEE Trans. Mob. Comput., 18 (12): 2941-2953 (2019)Exploiting Parallelism Opportunities with Deep Learning Frameworks., , , , and . ACM Trans. Archit. Code Optim., 18 (1): 9:1-9:23 (2021)Infinite Recommendation Networks: A Data-Centric Approach., , , and . NeurIPS, (2022)AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning., and . MICRO, page 1082-1096. IEEE, (2020)Chasing Carbon: The Elusive Environmental Footprint of Computing., , , , , , , and . CoRR, (2020)Understanding Capacity-Driven Scale-Out Neural Recommendation Inference., , , , , , and . CoRR, (2020)RecSSD: near data processing for solid state drive based recommendation inference., , , , , , and . ASPLOS, page 717-729. ACM, (2021)Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms., and . IISWC, page 171-180. IEEE Computer Society, (2014)Characterization and Throttling-Based Mitigation of Memory Interference for Heterogeneous Smartphones., , and . IISWC, page 22-33. IEEE Computer Society, (2015)