针对微型设备关键词识别模型，提出智能初始化方法提升多目标优化效率。
OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting
- 用多目标模拟退火生成初始解，引导优化向可行区域收敛
- 在相同资源限制下，相比随机等方法提升超体积指标37%以上
- 适合嵌入式语音识别模型设计，尤其关注内存与性能平衡的场景
语音触发界面依赖关键词识别（KWS）模型，在微控制器级硬件上需持续运行，受内存、延迟和功耗严格约束。设计此类模型不仅要求高识别准确率，还需在有限的Flash和SRAM预算内具备可预测的部署能力。贝叶斯优化虽能有效处理准确性-效率权衡，但对初始化高度敏感，尤其在TinyML低预算优化中表现不佳。本文提出目标感知代理初始化（OASI），通过多目标模拟退火生成帕累托偏置的初始解，引导代理模型朝可行的准确率-内存权衡方向优化，避免违反SRAM限制的配置。在TinyML KWS任务中，OASI相较于拉丁超立方、Sobol及随机初始化，在相同预算下显著提升超体积与收敛鲁棒性。基于STM32微控制器的软硬件协同实验验证了无需额外优化成本即可获得可部署、内存合规的模型。
原文摘要 · Abstract (English)
Voice-triggered interfaces rely on keyword spotting (KWS) models that must operate continuously under strict memory, latency, and energy constraints on microcontroller-class hardware. Designing such models therefore requires not only high recognition accuracy but also predictable deployability within limited Flash and SRAM budgets. Bayesian optimization is known to handle accuracy-efficiency trade-offs effectively in multi-objective optimization; however, it is highly sensitive to initialization, particularly in the low-budget regimes of TinyML model optimization. We propose Objective-Aware Surrogate Initialization (OASI), which seeds surrogate optimization with Pareto-biased solutions generated via multi-objective simulated annealing. Unlike space-filling or heuristic warm-start methods, OASI initializes the surrogate conditioning process with a bias toward feasible accuracy-memory trade-offs, thus avoiding SRAM-violating configurations. OASI improves hypervolume and convergence robustness over Latin hypercube, Sobol, and random initializations under the same budget constraints on a TinyML KWS problem. Hardware-in-the-loop experiments on STM32 microcontrollers verify the existence of deployable and memory-feasible models without incurring extra optimization costs.
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