在设备端运行轻量NAS，实时优化小模型以适应不同用户生物信号。
On-Device Neural Architecture Search
- 在设备上直接运行轻量NAS，动态搜索最优微型神经网络。
- 在ISL数据集上比现有方法少用63%内存，准确率高5.96个百分点。
- 适合人机交互中用户变化频繁的场景，也适用于设备故障诊断。
本文提出一种新型近传感器计算方法，即在部署设备上直接执行轻量级神经架构搜索（NAS），以找到最适合分析实时传感器数据的微型神经网络架构。该自适应能力在人机交互场景中尤为关键，当用户更换时，可通过引导式数据采集重新设计神经网络，有效应对个体间生物信号差异。为此设计了一种新型NAS，并在意大利手语数据集（ISL）——一组表面肌电（sEMG）信号——上进行了验证，使用多个嵌入式系统进行测试。此外，还在病例西储大学数据集（CWRU）上进一步验证，该数据集是智能故障诊断的基准。在Raspberry Pi 4上运行时，所提NAS表现超越现有技术：在ISL数据集上，内存占用减少0.63倍，准确率提升5.96个百分点；在CWRU数据集上，内存占用减少0.44倍，准确率提升0.2个百分点。
原文摘要 · Abstract (English)
This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.
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