Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a typical situation, BP-NN-based models are iteratively optimized in server machines with input data gathered from the edge devices. However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i.e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption. To address these issues, we propose ONLAD and its IP core, named ONLAD Core. ONLAD is highly optimized to perform fast sequential learning to follow concept drift in less than one millisecond. ONLAD Core realizes on-device learning for edge devices at low power consumption, which realizes standalone execution where data transfers between edge and server are not required. Experiments show that ONLAD has favorable anomaly detection capability in an environment that simulates concept drift. Evaluations of ONLAD Core confirm that the training latency is 1.95x~6.58x faster than the other software implementations. Also, the runtime power consumption of ONLAD Core implemented on PYNQ-Z1 board, a small FPGA/CPU SoC platform, is 5.0x~25.4x lower than them.

中文翻译：

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适用于边缘设备的基于神经网络的设备上学习异常检测器


半监督异常检测是一种通过学习正常数据的分布来识别异常的方法。基于反向传播神经网络（即 BP-NN）的方法最近因其良好的泛化能力而引起了人们的关注。在典型情况下，基于 BP-NN 的模型在服务器机器中使用从边缘设备收集的输入数据进行迭代优化。然而，(1) 迭代优化通常需要付出巨大努力来跟踪正常数据分布的变化（即概念漂移），以及 (2) 边缘和服务器之间的数据传输会带来额外的延迟和能耗。为了解决这些问题，我们提出了ONLAD及其IP核，名为ONLAD Core。 ONLAD 经过高度优化，可以执行快速顺序学习，以在不到一毫秒的时间内跟踪概念漂移。 ONLAD Core实现了边缘设备低功耗的在设备学习，实现了边缘设备与服务器之间不需要数据传输的独立执行。实验表明ONLAD在模拟概念漂移的环境中具有良好的异常检测能力。 ONLAD Core 的评估证实，训练延迟比其他软件实现快 1.95 倍~6.58 倍。此外，在小型 FPGA/CPU SoC 平台 PYNQ-Z1 板上实现的 ONLAD Core 的运行时功耗比它们低 5.0 倍~25.4 倍。