Deep neural networks with rate-based neurons have exhibited tremendous progress in the
last decade. However, the same level of progress has not been observed in research on …

Deep learning inference that needs to largely take place on the “edge” is a highly
computational and memory intensive workload, making it intractable for low-power …

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively
compute the continuous-time flow information of events produced from bio-inspired cameras …

Applications in the Internet of Video Things (IoVT) domain have very tight constraints with
respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages …

As an alternative sensing paradigm, dynamic vision sensors (DVSs) have been recently
explored to tackle scenarios where conventional sensors result in high data rate and …

Most successful computer vision models transform low-level features, such as Gabor filter
responses, into richer representations of intermediate or mid-level complexity for …

As an alternative sensing paradigm, dynamic vision sensors (DVS) have been recently
explored to tackle scenarios where conventional sensors result in high data rate and …

The computing industry has progressed through the rapid development of PCs in the 1990s,
the explosion of gaming in the 2000s, and the introduction of cloud and mobile computing in …

Deep neural networks have shown significant improvements in computer vision applications
over the last few years. Performance improvements have been brought about mostly by …