  Deploying deep models on embedded devices has been a challenging problem
since the great success of deep learning based networks. Fixed-point networks,
which represent their data with low bits fixed-point and thus give remarkable
savings on memory usage, are generally preferred. Even though current
fixed-point networks employ relative low bits (e.g. 8-bits), the memory saving
is far from enough for the embedded devices. On the other hand, quantization
deep networks, for example XNOR-Net and HWGQNet, quantize the data into 1 or 2
bits resulting in more significant memory savings but still contain lots of
floatingpoint data. In this paper, we propose a fixed-point network for
embedded vision tasks through converting the floatingpoint data in a
quantization network into fixed-point. Furthermore, to overcome the data loss
caused by the conversion, we propose to compose floating-point data operations
across multiple layers (e.g. convolution, batch normalization and quantization
layers) and convert them into fixedpoint. We name the fixed-point network
obtained through such integrated conversion as Integrated Fixed-point
Quantization Networks (IFQ-Net). We demonstrate that our IFQNet gives 2.16x and
18x more savings on model size and runtime feature map memory respectively with
similar accuracy on ImageNet. Furthermore, based on YOLOv2, we design
IFQ-Tinier-YOLO face detector which is a fixed-point network with 256x
reduction in model size (246k Bytes) than Tiny-YOLO. We illustrate the
promising performance of our face detector in terms of detection rate on Face
Detection Data Set and Bencmark (FDDB) and qualitative results of detecting
small faces of Wider Face dataset.
