README.md

AgileNN: Real-time neural network inference on extremely weak devices: agile offloading with explainable AI (MobiCom'22)

Introduction

This code repository provides training scripts of AgileNN. It leverages eXplainable AI (XAI) to enable fast and accurate neural network inference on extremely weak devices (e.g., MCUs) with wireless connections. It shifts the rationale of offloading from fixed to data-centric and agile. Please refer to our paper for details.

Requirements

• Python 3.7+
• tensorflow 2
• tensorflow-datasets
• tensorflow-addons
• tiny-imagenet-tfds
• tqdm

Usage

Below shows an example of training AgileNN on CIFAR100 dataset. First pre-train Reference NN on CIFAR100 dataset:

python train_evaluator.py --dataset cifar100

Then train AgileNN on CIFAR100 dataset:

python main.py --dataset cifar100 \
               --split_ratio 0.2 \
               --rho 0.8 \
               --klambda 0.8 \
               --num_centroids 8 \
               --channel_remapping None

Note: channel_remapping is an optional preprocessing step. It can sometimes improve training stability. Please refer to feature_extractor_prep.py and Section 5 in our paper for details.

It should achieve ~73% final accuracy and ~83% skewness. Computing gradients on gradients can be expensive, so you may need powerful GPUs with large memory.

The trained modules of AgileNN can be converted to tflite or tflite-micro format for deployment. The quantized remote features can be further compressed by lossless algorithms, e.g., LZW and Huffman.

Citation

@inproceedings{huang2022real,
  title={Real-time neural network inference on extremely weak devices: agile offloading with explainable AI},
  author={Huang, Kai and Gao, Wei},
  booktitle={Proceedings of the 28th Annual International Conference on Mobile Computing And Networking},
  pages={200--213},
  year={2022}
}