Event-based optical flow on neuromorphic hardware

Event-based sensing, i.e. the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition using standard cameras. Recently, we proposed a novel biologically inspired efficient motion detector for such event-based input streams and demonstrated how a canonical neural circuit can improve such representations using normalization and feedback. In this contribution, we suggest how such a motion detection scheme is defined by utilizing a canonical neural circuit corresponding to the resolution of cortical columns. In addition, we develop a mapping of key computational elements of this circuit model onto neuromorphic hardware. In particular, we focus on the recently developed TrueNorth chip architecture by IBM to realize a real-time, energy-efficient and adjustable neuromorphic optical flow detector. We demonstrate the function of the computations of the canonical model and its approximate neuromorphic realization.