Real-time anomaly detection for streaming data is a desirable feature for mobile devices or unmanned systems. The key challenge is how to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of large-scale neural models. Meanwhile, inspired by the operation and the massive parallel structure of human brain, carefully structured inference model has been demonstrated to give superior detection quality than many traditional models while facilitates neuromorphic implementation. Implementing inference based anomaly detection on the neurosynaptic processor is not straightforward due to hardware limitations. This work presents a design flow and component library that flexibly maps learned detection network to the TrueNorth architecture. Instead of traditional rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the results accuracy. A Corelet library, NeoInfer-TN, is developed for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy and throughput/energy. We evaluate the system using intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10⁸ operations per watt-second.