  Event cameras offer low-latency and data compression for visual applications,
through event-driven operation, that can be exploited for edge processing in
tiny autonomous agents. Robust, accurate and low latency extraction of highly
informative features such as corners is key for most visual processing. While
several corner detection algorithms have been proposed, state-of-the-art
performance is achieved by luvHarris. However, this algorithm requires a high
number of memory accesses per event, making it less-than ideal for low-latency,
low-energy implementation in tiny edge processors. In this paper, we propose a
new event-driven corner detection implementation tailored for edge computing
devices, which requires much lower memory access than luvHarris while also
improving accuracy. Our method trades computation for memory access, which is
more expensive for large memories. For a DAVIS346 camera, our method requires
~3.8X less memory, ~36.6X less memory accesses with only ~2.3X more computes.
