@inproceedings{ganesan2020case,
  title={A Case for Generalizable DNN Cost Models for Mobile Devices},
  author={Ganesan, Vinod and Selvam, Surya and Sen, Sanchari and Kumar, Pratyush and Raghunathan, Anand},
  booktitle={2020 IEEE International Symposium on Workload Characterization (IISWC)},
  pages={169--180},
  year={2020},
  organization={IEEE}
}

The android app is derived from the basic pytorch-demo-app, with custom enhancements and the integration of tflite-runtime. You can follow the steps provided by PyTorch to build the app. Stay tuned for more details on tweaking the app to generate inference time.

cd char_framework/net_generator/mobileOptGen
python MobileGen.py

cd cost_model
python gen_cost_models.py --sampling_type SAMPLE --learning_type LEARN  --name NAME  --numSamples SAMPLE --model MODEL 

• Command line options

--sampling_type (required): The sampling type required for choosing the Signature Set. "random", "mutual_info", "spearmanCorr", and "statistical" are valid choices.

--learning_type (required): The way in which the cost model is trained. "individual", "combined", and "collaborative" are valid choices.

--name (required): Name of the run.

--numSamples (required): Number of samples used to form the Signature Set.

--model (required): The cost model used. "xgb" and "lstm" are valid choices.

• Python 3+
• XGBoost
• Tensorflow 2+
• PyTorch
• ONNX
• torchprofile
• Android Studio (For measuring inference latency)