Real-time anomaly detection for streaming data using burst code on a neurosynaptic processor

Qiuwen Chen, Qinru Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages205-207
Number of pages3
ISBN (Electronic)9783981537093
DOIs
StatePublished - May 11 2017
Event20th Design, Automation and Test in Europe, DATE 2017 - Swisstech, Lausanne, Switzerland
Duration: Mar 27 2017Mar 31 2017

Other

Other20th Design, Automation and Test in Europe, DATE 2017
CountrySwitzerland
CitySwisstech, Lausanne
Period3/27/173/31/17

    Fingerprint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Cite this

Chen, Q., & Qiu, Q. (2017). Real-time anomaly detection for streaming data using burst code on a neurosynaptic processor. In Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017 (pp. 205-207). [7926983] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/DATE.2017.7926983