Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and non-negotiable accuracy thresholds must all be satisfied simultaneously. Existing compression and optimization techniques can trade one resource for another, but rarely improve both accuracy and model size at the same time. This paper presents the application of Perforated Backpropagation to keyword spotting on the Edge Impulse platform, an experiment that won the Best Model award at the Edge Impulse 2025 Hackathon in December 2025. By adding artificial Dendrite Nodes to a standard convolutional neural network trained on the Edge Impulse keyword spotting tutorial pipeline, we demonstrate that dendritic models outperform traditional architectures at every level of parameter count and at every accuracy threshold tested across 800 hyperparameter trials. The best dendritic model achieved a test accuracy of 0.933 with only 1,500 parameters, versus the baseline accuracy of 0.921 requiring approximately 4,000 parameters. These results suggest that Perforated Backpropagation is a powerful addition to the edge AI engineer's toolkit, offering simultaneous gains in both model quality and deployment efficiency.

翻译：边缘机器学习面临着一系列云端模型部署中不存在的独特约束：严格的内存预算、有限的计算资源以及不可妥协的准确率阈值必须同时满足。现有的压缩与优化技术可以在一种资源与另一种资源之间进行权衡，但很少能同时提升准确率并减小模型规模。本文介绍了在Edge Impulse平台上将穿孔反向传播算法应用于关键词检测的实验，该实验于2025年12月获得了Edge Impulse 2025黑客马拉松最佳模型奖。通过在Edge Impulse关键词检测教程流水线训练的标准卷积神经网络中引入人工树突节点，我们证明树突状模型在800次超参数试验中，在每个参数数量级别和每个测试准确率阈值下均优于传统架构。最佳树突模型在仅使用1500个参数的情况下达到了0.933的测试准确率，而基线模型需要约4000个参数才能达到0.921的准确率。这些结果表明，穿孔反向传播算法是边缘人工智能工程师工具箱中的强大补充，能够同时提升模型质量和部署效率。