让模型和数据配置一起优化，打造更小更准的关键词识别系统
Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
- 联合搜索模型结构与数据参数，实现协同优化
- 在关键词识别任务中生成轻量且高精度的模型
- 适合资源受限场景下的智能语音系统设计
机器学习的成功越来越受到其巨大资源消耗的制约，推动了像TinyML这类高效范式的发展。然而，设计TinyML系统固有的复杂性阻碍了其广泛应用。为降低这种复杂性，我们提出「数据感知可微神经架构搜索」。与传统可微神经架构搜索不同，我们的方法将数据配置参数纳入搜索空间，与模型架构选择并行优化。该方法能够协同优化模型架构与输入数据特征，有效平衡资源使用与系统性能，适用于TinyML应用。关键词识别任务的初步结果表明，这一新方法可生成体积小但准确率高的系统。
原文摘要 · Abstract (English)
The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexity of designing TinyML systems hampers their broad adoption. To reduce this complexity, we introduce "Data Aware Differentiable Neural Architecture Search". Unlike conventional Differentiable Neural Architecture Search, our approach expands the search space to include data configuration parameters alongside architectural choices. This enables Data Aware Differentiable Neural Architecture Search to co-optimize model architecture and input data characteristics, effectively balancing resource usage and system performance for TinyML applications. Initial results on keyword spotting demonstrate that this novel approach to TinyML system design can generate lean but highly accurate systems.
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