The BMNET library is designed to convert the neural networks defined by CAFFE to target instructions. It seems like a compiler which translates high-level language into machine instruc- tions. It also contains three phase which are the front end, the optimizer and the back end. The front end parses source code, extracts network prototxt and weights. The optimizer is responsible for doing a broad variety of transformations to try to improve the code’s running time. The back end (also known as the code generator) then maps the code onto the target instruction set. In the BM1880 platform,we add a new feature called INT8 computation,it can provide better performance such as inference speedup. INT8 computation need an calibration table to modify network parameter,you can refer section 2 for how to generate a network’s calibration table. Refer this document,you can convert a network from FP32 to INT8 without significant accuracy loss.