shaojiawei07/edge_inference_three-step_framework

Edge_Inference_three-step_framework

This code is for the paper: "communication-computation tradeoffs in resource-constrained edge inference".

Framework

We propose a three-step framework to reduce the end-to-end latency in the edge inference. The structure is shown as follows:

Implementation

The three-step framework is implemented based on ResNet18. The network is split at the end of each building block.

Dependency

Pytorch 1.6.0
Torchvision 0.7.0
Numpy 1.6.12

Dataset

CIFAR-10

How to Run

0. Pretrain a ResNet model from scratch or download from here.
1. model_pruning.py uses magnitude-based pruning method to compress the on-device model with parameter -sp1, -sp2, -sp3, -sp4, and -sp5 to denote the Sparsity ratios in different building blocks. The pruned model is named sp1_xx_sp2_xx_sp3_xx_sp4_xx_test_acc_xxxx.
2. feature_compression.py compresses the intermediate feature, which loads the pruned model by -load, and selects the split point by -split. Besides, -hid_dim determines the intermediate feature dimension, and -bit n means n-bit quantization.

Citation

@article{shao2020communication,
  title={Communication-computation trade-off in resource-constrained edge inference},
  author={Shao, Jiawei and Zhang, Jun},
  journal={IEEE Communications Magazine},
  volume={58},
  number={12},
  pages={20--26},
  year={2020},
  publisher={IEEE}
}

edge_inference_three-step_framework's Issues

How to solve the "Files already downloaded and verified" error when running the code

Can it be provided (numpy1.6.12).

Can it be provided (numpy1.6.12).

partially initialized module 'resnet' has no attribute 'resnet18' (most likely due to a circular import). Did you mean: 'resnet'，how to solve it