Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences
Machine learning models are widely integrated into modern mobile apps to analyze user behaviors and deliver personalized services. Ensuring low-latency on-device model execution is critical for maintaining high-quality user experiences. While prior research has primarily focused on accelerating model inference with given input features, we identify an overlooked bottleneck in real-world on-device model execution pipelines: extracting input features from raw application logs. In this work, we explore a new direction of feature extraction optimization by analyzing and eliminating redundant extraction operations across different model features and consecutive model inferences. We then introd...
| Authors | Chen Gong, Zhenzhe Zheng, Yiliu Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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