IEEE Trans Neural Netw Learn Syst. 2018 May;29(5):1622-1636. doi: 10.1109/TNNLS.2017.2676110. Epub 2017 Mar 17.
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.
高性能计算技术的发展使大规模实现神经形态模型成为可能，并将计算智能研究推向了一个新时代。在机器学习应用中，由于需要检测精度和实时性能，异常流的无监督检测尤其具有挑战性。设计一个利用多核系统不断增长的计算能力的计算框架，同时保持对异常的高灵敏度和特异性，是一个紧迫的研究课题。在本文中，我们提出了异常识别与检测（AnRAD），这是一种基于生物启发的检测框架，执行概率推断。我们分析了特征的依赖性，并开发了一种自组织方法，使用无标签数据学习有效的虚构网络。该网络能够快速进行增量学习，使用流数据不断改进知识库。与现有的几种异常检测方法相比，我们的方法提供了具有竞争力的检测质量。此外，我们利用了 AnRAD 框架的大规模并行结构。我们在图形处理单元和 Xeon Phi 协处理器上实现检测算法，与通用微处理器上的顺序实现相比，都获得了显著的加速。该框架为多样化知识环境中的并发数据流提供实时服务，并且可以应用于具有多个局部模式的大型问题。实验结果表明，该框架具有很高的计算性能和内存效率。对于车辆行为检测，该框架能够使用单个商用协处理器实时监控多达 16000 个车辆（数据流）及其交互，并且每个测试对象的用时不到 0.2 毫秒。最后，检测网络被移植到我们的脉冲神经网络模拟器中，以展示适应新兴神经形态架构的潜力。
