#6355. Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

November 2026publication date
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Journal’s subject area:
Information Systems;
Software;
Neuroscience (all);
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Abstract:
The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) 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 methods 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.
Keywords:
embedded devices; FP-growth; heterogeneous computing; low power; parallel and distributed computing; pattern mining; performance optimization; spike train analysis

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