  The performance of object detection systems in automotive solutions must be
as high as possible, with minimal response time and, due to the often
battery-powered operation, low energy consumption. When designing such
solutions, we therefore face challenges typical for embedded vision systems:
the problem of fitting algorithms of high memory and computational complexity
into small low-power devices. In this paper we propose PowerYOLO - a mixed
precision solution, which targets three essential elements of such application.
First, we propose a system based on a Dynamic Vision Sensor (DVS), a novel
sensor, that offers low power requirements and operates well in conditions with
variable illumination. It is these features that may make event cameras a
preferential choice over frame cameras in some applications. Second, to ensure
high accuracy and low memory and computational complexity, we propose to use
4-bit width Powers-of-Two (PoT) quantisation for convolution weights of the
YOLO detector, with all other parameters quantised linearly. Finally, we
embrace from PoT scheme and replace multiplication with bit-shifting to
increase the efficiency of hardware acceleration of such solution, with a
special convolution-batch normalisation fusion scheme. The use of specific
sensor with PoT quantisation and special batch normalisation fusion leads to a
unique system with almost 8x reduction in memory complexity and vast
computational simplifications, with relation to a standard approach. This
efficient system achieves high accuracy of mAP 0.301 on the GEN1 DVS dataset,
marking the new state-of-the-art for such compressed model.
