Quantization and Reconstruction algorithm for Quantized and Reconstructed Deep Neural Networks

Code example for the FPL 2018 short paper. We modify the original caffe to implement our QR-DNN algorithm. And we also thank the example code provied by Ristretto.

Prerequisites

• NVIDIA GPU + CUDA + CuDNN
• python2.7
• Imagenet dataset

QR-DNN usage

Preparsion

make all

make pycaffe

Command to generate a fixed-point AlexNet model with 8-bit actiavtions and weight,

python python/merge_all.py --model=test/train_val.prototxt --mode=dynamic_fixed_point --solver_first=test/train_alex.solver

Command to generate a logrithmic AlexNet model with 8-bit actiavtions and 4-bit weight,

python python/merge_all.py --model=test/train_val.prototxt --mode=Power-of-two --solver_first=test/train_alex.solver --solver_second=test/train_alex_second.solver

Command to generate a fixed-point ResNet-50 model with 8-bit actiavtions and weight,

python python/merge_all.py --model=test_resnet/ResNet_50_train_val.prototxt --weight=test_resnet/ResNet-50-model.caffemodel --mode=dynamic_fixed_point --skip_first_phase=yes