  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.
