  This paper presents a methodology to separate the quantization process from
the hardware-specific model compilation stage via a pre-quantized deep learning
model description in standard ONNX format. Separating the quantization process
from the model compilation stage enables independent development. The
methodology is expressive to convey hardware-specific operations and to embed
key quantization parameters into a ONNX model which enables hardware/software
co-design. Detailed examples are given for both MLP and CNN based networks,
which can be extended to other networks in a straightforward fashion.
