Covariance-Guided Resource Adaptive Learning for Efficient Edge Inference
For deep learning inference on edge devices, hardware configurations achieving the same throughput can differ by 2$\times$ in power consumption, yet operators often struggle to find the efficient ones without exhaustive profiling. Existing approaches often rely on inefficient static presets or require expensive offline profiling that must be repeated for each new model or device. To address this problem, we present CORAL, an online optimization method that discovers near-optimal configurations without offline profiling. CORAL leverages distance covariance to statistically capture the non-linear dependencies between hardware settings, e.g., DVFS and concurrency levels, and performance metr...
| Authors | Ahmad N. L. Nabhaan, Zaki Sukma, Rakandhiya D. Rachmanto et al. |
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| Primary category | cs.DC |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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