Node-wise Feature Encoding for Neural Performance Prediction
As neural networks are increasingly deployed on resource constrained edge devices, accurate prediction of latency and energy is critical for efficient neural architecture search. Existing GNN and transformer based predictors achieve strong results but largely ignore node-level computational cost, limiting their ability to model performance critical operations. To address this, we introduce FeatureFormer, a neural performance predictor that incorporates explicit node-wise encodings of FLOPs, parameter counts, and memory proxies within a gated graph attention architecture. We also present NNEQ, a new large-scale energy consumption dataset that enables unified evaluation of latency and energ...
| Authors | Matthew Grenier, William Hammer, Andrew Heuer et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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