Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4× and the feature extraction cost by 14.6× compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6× and 6.8×, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6× and feature computation cost by 5.1×. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding.

中文翻译：

---

ResOT：资源有效的倾斜树，用于神经信号分类。

可以用最少的计算和内存资源在芯片上实现的分类器对于新兴应用（如医疗和物联网设备）中的边缘计算至关重要。本文介绍了一种基于倾斜决策树的机器学习模型，可以对神经植入物进行资源高效的分类。通过将模型压缩与概率路由集成在一起并实现成本意识学习，与最新模型相比，我们提出的模型可以显着减少内存和硬件成本，同时保持分类的准确性。我们在三个神经分类任务上通过高能效正则化（ResOT-PE）训练了资源高效的斜树，以评估性能，内存和硬件要求。在癫痫发作检测任务中，我们能够将模型大小减少3。使用10名癫痫患者的颅内脑电图，与增强树的集成相比，特征提取成本高4倍，特征提取成本高14.6倍。在第二个实验中，我们使用植入了深脑刺激（DBS）装置的12位患者的局部场电势，测试了用于震颤检测的帕金森氏病的ResOT-PE模型。我们获得了与最新的增强树集成类似的分类性能，同时将模型大小和特征提取成本分别降低了10.6倍和6.8倍。我们还使用来自9个受试者的ECoG记录对6类手指运动检测任务进行了测试，将模型尺寸减小了17.6倍，并将特征计算成本降低了5.1倍。