TinyML中动态集成选择提升分类精度，兼顾推理延迟与能耗
On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
- 基于聚类的动态集成方法，按需选择分类器组合
- 分类器池越大，准确率越高，平均推理时间增加约18%
- 适合资源受限设备上需要灵活权衡精度与效率的场景
TinyML技术的进展带来了在推理时延与分类质量之间取得平衡的新挑战。TinyML系统受计算、内存和能量的严格限制，因此在嵌入式平台部署机器学习应用时需采用专门优化技术。尽管深度神经网络广泛应用，但动态集成选择（DES）方法同样具有潜力。本研究针对TinyML系统中的多类别图像识别任务，探索了一种基于聚类的DES方法。该方法可调节分类准确率，从而影响每次推理的延迟和能耗。我们实现了专为嵌入式系统设计的TinyDES-Clustering库。实验表明，更大的分类器池能提升分类准确率，但也导致平均推理时间上升。
原文摘要 · Abstract (English)
The recent progress in TinyML technologies triggers the need to address the challenge of balancing inference time and classification quality. TinyML systems are defined by specific constraints in computation, memory and energy. These constraints emphasize the need for specialized optimization techniques when implementing Machine Learning (ML) applications on such platforms. While deep neural networks are widely used in TinyML, the exploration of Dynamic Ensemble Selection (DES) methods is also beneficial. This study examines a DES-Clustering approach for a multi-class computer vision task within TinyML systems. This method allows for adjusting classification accuracy, thereby affecting latency and energy consumption per inference. We implemented the TinyDES-Clustering library, optimized for embedded system limitations. Experiments have shown that a larger pool of classifiers for dynamic selection improves classification accuracy, and thus leads to an increase in average inference time on the TinyML device.
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