Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

Frontiers in Neuroinformatics, vol. 15, pp. 723406

Abstract

The SPADE (spatio-temporal S pike PA ttern D etection and E valuation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE , which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

Authors 7

1. Florian Porrmann corresponding

   Bielefeld University

   Affiliation as printed

   Cognitronics and Sensor Systems, CITEC, Bielefeld University, Bielefeld, Germany

2. Sarah Pilz

   Bielefeld University

   Affiliation as printed

   Cognitronics and Sensor Systems, CITEC, Bielefeld University, Bielefeld, Germany

3. Alessandra Stella Aachen

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Center, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

4. Alexander Kleinjohann Aachen

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Center, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

5. Michael Denker

   Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Center, Jülich, Germany

6. Jens Hagemeyer

   Bielefeld University

   Affiliation as printed

   Cognitronics and Sensor Systems, CITEC, Bielefeld University, Bielefeld, Germany

7. Ulrich Rückert

   Bielefeld University

   Affiliation as printed

   Cognitronics and Sensor Systems, CITEC, Bielefeld University, Bielefeld, Germany