  "Lightweight convolutional neural networks" is an important research topic in
the field of embedded vision. To implement image recognition tasks on a
resource-limited hardware platform, it is necessary to reduce the memory size
and the computational cost. The contribution of this paper is stated as
follows. First, we propose an algorithm to process a specific network
architecture (Condensation-Net) without increasing the maximum memory storage
for feature maps. The architecture for virtual feature maps saves 26.5% of
memory bandwidth by calculating the results of cross-channel pooling before
storing the feature map into the memory. Second, we show that cross-channel
pooling can improve the accuracy of object detection tasks, such as face
detection, because it increases the number of filter weights. Compared with
Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and
1.5% for full-precision networks when the false-positive rate is 0.1. Last but
not the least, the analysis results show that the overhead to support the
cross-channel pooling with the proposed hardware architecture is negligible
small. The extra memory cost to support Condensation-Net is 0.2% of the total
size, and the extra gate count is only 1.0% of the total size.
