  Object detection has made impressive progress in recent years with the help
of deep learning. However, state-of-the-art algorithms are both computation and
memory intensive. Though many lightweight networks are developed for a
trade-off between accuracy and efficiency, it is still a challenge to make it
practical on an embedded device. In this paper, we present a system-level
solution for efficient object detection on a heterogeneous embedded device. The
detection network is quantized to low bits and allows efficient implementation
with shift operators. In order to make the most of the benefits of low-bit
quantization, we design a dedicated accelerator with programmable logic. Inside
the accelerator, a hybrid dataflow is exploited according to the heterogeneous
property of different convolutional layers. We adopt a straightforward but
resource-friendly column-prior tiling strategy to map the computation-intensive
convolutional layers to the accelerator that can support arbitrary feature
size. Other operations can be performed on the low-power CPU cores, and the
entire system is executed in a pipelined manner. As a case study, we evaluate
our object detection system on a real-world surveillance video with input size
of 512x512, and it turns out that the system can achieve an inference speed of
18 fps at the cost of 6.9W (with display) with an mAP of 66.4 verified on the
PASCAL VOC 2012 dataset.
