BlockConv

This repository serves as the official code release of the paper Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA (pubilished at TCAD 2021).

Block convolution is a hardware-friendly, simple, yet efficient convolution operation that can completely avoid the off-chip transfer of intermediate feature maps at runtime. The fundamental idea of block convolution is to eliminate the dependency of feature map tiles in the spatial dimension when spatial tiling is used, which is realized by splitting a feature map into independent blocks so that convolution can be performed separately on individual blocks.

Installation

• Python version >= 3.8
• Pytorch version >= 1.8

# create conda environment
conda create -n BlockConv 
conda activate BlockConv
conda install pytorch torchvision cudatoolkit=10.2
pip install torchnet tqdm tabulate gitpython tensorboard

# install from source code
git clone https://github.com/zejiangp/BlockConv.git
cd BlockConv

Training from scratch

• --arch: block_resnet18, block_resnet50, block_vgg16, block_mobilenet
• --padding_mode: constant (equal to zero padding), replicate, reflect
• --type: 0 (Fixed blocking), 1 (hierarchical blocking)

For example, if we want to train a resnet18 with block size 28, fixed blocking mode, and zero padding from scratch, the command as below:

python classification.py \
    ./data/ilsvrc12   \
    --dataset=imagenet   \
    --out_dir=logs/  \
    --gpus=0,1,2,3   \
    --arch=block_resnet18\
    --name=resnet18_F28_constant_scratch   \
    --batch_size=128   \
    -j=32   \
    --epochs=90 \
    --lr=0.1   \
    --wd=1e-4  \
    --momentum=0.9 \
    --milestones=30,60 \
    --block_size 28,28 \
    --padding_mode constant \
    --type 0 \
    --do_train \
    --do_eval

Fine-tuning

Another way to get a model using block convolution is fine-tuning from the pre-trained model:

python classification.py \
    ./data/ilsvrc12   \
    --dataset=imagenet   \
    --out_dir=logs/  \
    --gpus=0,1,2,3   \
    --arch=block_resnet18\
    --name=resnet18_F28_constant   \
    --batch_size=128   \
    -j=32   \
    --epochs=30 \
    --lr=0.001   \
    --wd=1e-4  \
    --momentum=0.9 \
    --milestones=10,20 \
    --block_size 28,28 \
    --padding_mode constant \
    --type 0 \
    --resume_from logs/resnet18_baseline.pth.tar \
    --reset_optimizer \
    --do_train \
    --do_eval

Hyperparamter

strategy	model	epochs	batch size	learning rate	weight decay	milestones
Training from scratch	resnet18 resnet 50 vgg16 mobilenet	90 90 105 300	128 128 256 128	0.1 0.1 0.01 0.0001	1e-4 1e-4 5e-4 5e-5	30,60 30, 60 30, 60, 90 -
Fine-tuning	resnet18 resnet 50 vgg16 mobilenet	30 30 20 50	128 128 256 128	0.001 0.001 0.001 0.0001	1e-4 1e-4 1e-4 5e-5	10, 20 10, 20 8, 16 -

Evaluation

python classification.py \
    ./data/ilsvrc12   \
    --dataset=imagenet   \
    --out_dir=test_logs/  \
    --gpus=0   \
    --arch=block_vgg16    \
    --name=test_vgg   \
    --batch_size=128   \
    -j=32   \
    --block_size 28,28 \
    --padding_mode constant \
    --type 0 \
    --do_eval \
    --resume_from logs/vgg16_finetune_F28_zero.pth.tar

Model Accuracy

We provide pre-trained models for evaluations here.

• TOP-1 accuracy on ImageNet classification task.

model	baseline	Scratch	Fine-tuning
vgg16	71.59%	70.47%	71.45%
resnet18	70.60%	69.94%	71.21%
resnet50	75.86%	75.42%	76.67%
mobilenetv1	72.29%	72.05%	71.76%

• Top-1 accuracy of blocked networks with respect to blocking ratio under fixed blocking (F) and hierarchical blocking (H).

model	H2x2	H4x4	H8x8	H16x16	F112	F56	F28	F14
vgg16	70.14%	70.28%	70.76%	71.18%	71.81%	71.74%	71.45%	70.48%
resnet18	70.06%	70.67%	71.12%	70.82%	71.60%	71.37%	71.21%	70.20%
mobilenetv1	69.96%	71.49%	71.53%	71.50%	72.16%	71.89%	71.76%	71.13%

• Impact of block padding on classification accuracy.

model	zero	replicate	reflect
vgg16	71.45%	70.90%	70.22%
resnet18	71.21%	70.92%	70.61%
resnet50	76.67%	76.71%	76.47%
mobilenetv1	71.76%	71.92%	71.58%

Citation

If you found the library useful for your work, please kindly cite our work:

@article{Gangli2022BlockConv,  
    author={Li, Gang and Liu, Zejian and Li, Fanrong and Cheng, Jian},  
    journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},   
    title={Block Convolution: Toward Memory-Efficient Inference of Large-Scale CNNs on FPGA},   
    year={2022},  
    volume={41},  
    number={5},  
    pages={1436-1447},  
    doi={10.1109/TCAD.2021.3082868}
}