OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting

arXiv:2512.19739 · cs.LG, cs.SD · Submitted 2025-12-17 · Read on arXiv

cs.LG, cs.SD

Submitted: 2025-12-17

Updated: 2026-03-04

Code: https://github.com/sumansamui/OASI-MOBO-TinyML

Terminology

• Accuracy — 24× in this paper · explained in 1 episode
• Bayesian Optimization — 9× in this paper · explained in 3 episodes
• Multi-objective Optimization — 8× in this paper · explained in 4 episodes
• Objective-Aware Surrogate Initialization — 5× in this paper
• Acquisition Function — 2× in this paper · explained in 1 episode
• Deployability Index — 2× in this paper
• Resource Constraints — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latin Hypercube Sampling — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

Sources

• Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
• Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

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