  Neuromorphic vision sensing (NVS)\ devices represent visual information as
sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to
changes in scene reflectance. Unlike conventional active pixel sensing (APS),
NVS allows for significantly higher event sampling rates at substantially
increased energy efficiency and robustness to illumination changes. However,
object classification with NVS streams cannot leverage on state-of-the-art
convolutional neural networks (CNNs), since NVS does not produce frame
representations. To circumvent this mismatch between sensing and processing
with CNNs, we propose a compact graph representation for NVS. We couple this
with novel residual graph CNN architectures and show that, when trained on
spatio-temporal NVS data for object classification, such residual graph CNNs
preserve the spatial and temporal coherence of spike events, while requiring
less computation and memory. Finally, to address the absence of large
real-world NVS datasets for complex recognition tasks, we present and make
available a 100k dataset of NVS recordings of the American sign language
letters, acquired with an iniLabs DAVIS240c device under real-world conditions.
