TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials

• URL: http://arxiv.org/abs/2405.12179v3
• Date: Fri, 31 May 2024 18:29:13 GMT
• Title: TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials
• Authors: Yan Ru Pei, Olivier Coenen,
• Abstract summary: We focus on interfacing these networks with event-based data to perform online classification and detection with low latency. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs.
• Score: 1.1970409518725493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a neural network named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), belonging to the TENNs (Temporal Neural Networks) architecture. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99.59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100% with a small additional output filter; 2) 99.58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0.556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset.

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