# DiSNet

**Repository Path**: yangyi0219/DiSNet

## Basic Information

- **Project Name**: DiSNet
- **Description**: 边缘设备的协同推理
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-20
- **Last Updated**: 2025-11-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic Edge Devices
### MICRO-SPLIT DEEP LEARNING
Distributed micro-split deep learning enables flexible partitioning and distributed computing of deep neural networks in heterogeneous dynamic IoT for low-latency and energy-efficient cooperative Deep Learning operations. DISNET iteratively combines vertical (layerwise) and horizontal DNN partitioning for flexible, distributed, and parallel execution of neural network models in IoT systems with heterogeneous computing capabilities and network conditions without compromising accuracy.

### Code Structure

The repository contains the source code of DiSNet. The code is organized as follows: 
The folder `main` contains the files for the core implementation of the DiSNet framework. 
Test deep neural network models are included in the `models` folder.
The file `network.py` contains the implementation of the IoT mesh networks for testing, saved in the `networks` folder.
In the file `main/model_convert.py` modify the first part of the model_path
`model_path = "/home/eric/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth"`.

### Running

Create an instance of the Vgg-16 model by running the `model_vgg16.py` file in the `main` folder.

```
python3 models/model_vgg16.py
```

To generate the parameters of a pre-trained model, run the `model_convert.py` file.

```
python3 main/model_convert.py
```

Run the file `run_DiSNet.py` to test the inference time, energy consumption, and inference accuracy.

```
python3 main/run_DiSNet.py
```
Paper: https://doi.org/10.1109/JIOT.2023.3313514

## Citation

Please cite the paper as follows: Samikwa, Eric, Antonio Di Maio, and Torsten Braun. "DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT."  IEEE Internet of Things journal (2023). 
```
@article{samikwa2023disnet,
  title={Disnet: Distributed micro-split deep learning in heterogeneous dynamic iot},
  author={Samikwa, Eric and Di Maio, Antonio and Braun, Torsten},
  journal={IEEE Internet of Things journal},
  year={2023},
  publisher={IEEE}
}
```