优化线性UCB算法，让低功耗设备实时决策更高效
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems
- 用数学公式简化模型复杂度，减少计算开销
- 通过向量化加速矩阵运算，执行速度提升显著
- 适合资源受限的边缘智能系统，如物联网设备
随着物联网发展，将人工智能算法嵌入资源受限设备以实现无需依赖云端的实时自主决策变得愈发重要。然而，受限于计算能力、内存和能耗，复杂算法在嵌入式设备上的实现面临挑战。本文提出针对两种线性UCB上下文老虎机算法的算法与硬件优化方案：基于Sherman-Morrison-Woodbury公式进行算法改进以降低模型复杂度，并利用向量化加速提升矩阵运算效率。我们分别分析各项优化效果，再整合为双路径策略。实验表明，该方法显著降低执行时间和能耗，在低功耗、实时性要求高的边缘计算环境中具有显著优势。
原文摘要 · Abstract (English)
As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on centralized cloud servers. However, implementing and executing complex algorithms in embedded devices poses significant challenges due to limited computational power, memory, and energy resources. This paper presents algorithmic and hardware techniques to efficiently implement two LinearUCB Contextual Bandits algorithms on resource-constrained embedded devices. Algorithmic modifications based on the Sherman-Morrison-Woodbury formula streamline model complexity, while vector acceleration is harnessed to speed up matrix operations. We analyze the impact of each optimization individually and then combine them in a two-pronged strategy. The results show notable improvements in execution time and energy consumption, demonstrating the effectiveness of combining algorithmic and hardware optimizations to enhance learning models for edge computing environments with low-power and real-time requirements.
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