EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving
As large language models span dense, mixture-of-experts, and state-space architectures and are deployed on heterogeneous accelerators under increasingly diverse multimodal workloads, optimising inference energy has become as critical as optimizing latency and throughput. Existing approaches either treat latency as an energy proxy or rely on data-hungry black-box surrogates. Both fail under varying parallelism strategies: latency and energy optima diverge in over 20% of configurations we tested, and black-box surrogates require hundreds of profiling samples to generalize across model families and hardware. We present EnergyLens, which uses symbolic regression as a structure-discovery tool ...
| Authors | Vittorio Palladino, Gianluca Palermo, Michael E. Papka et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 13 May 2026 |
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