  Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found
increased attention in high-resolution and high-speed applications. A lot of
work has been conducted to enhance recognition accuracy. However, the essential
topic of recognition delay or time efficiency is largely under-explored. In
this paper, we present a spiking learning system that uses the spiking neural
network (SNN) with a novel temporal coding for accurate and fast object
recognition. The proposed temporal coding scheme maps each event's arrival time
and data into SNN spike time so that asynchronously-arrived events are
processed immediately without delay. The scheme is integrated nicely with the
SNN's asynchronous processing capability to enhance time efficiency. A key
advantage over existing systems is that the event accumulation time for each
recognition task is determined automatically by the system rather than pre-set
by the user. The system can finish recognition early without waiting for all
the input events. Extensive experiments were conducted over a list of 7 LiDAR
and DVS datasets. The results demonstrated that the proposed system had
state-of-the-art recognition accuracy while achieving remarkable time
efficiency. Recognition delay was shown to reduce by 56.3% to 91.7% in various
experiment settings over the popular KITTI dataset.
