Sparsity-aware roofline models are required for accurate SpMM performance prediction because matrix structure alters arithmetic intensity and a single unified model fails across patterns like block, banded, scale-free, and random.
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The paper introduces CoCaR for joint submodel caching and request routing with dynamic DNNs in MEC, reporting 46% higher average inference precision in simulations and at least 32.3% QoE gain for its online variant.
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Sparsity-Aware Roofline Models for Sparse Matrix-Matrix Multiplication
Sparsity-aware roofline models are required for accurate SpMM performance prediction because matrix structure alters arithmetic intensity and a single unified model fails across patterns like block, banded, scale-free, and random.
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Joint Optimization of DNN Model Caching and Request Routing in Mobile Edge Computing
The paper introduces CoCaR for joint submodel caching and request routing with dynamic DNNs in MEC, reporting 46% higher average inference precision in simulations and at least 32.3% QoE gain for its online variant.