Multi-Precision Policy Enforced Training (MuPPET)

This is the open-sourced implementation of multi-precision training connected to the paper "Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs" that was published in ICML 2020. If you reference this work in a publication, we would appreciate you using the following citation:

@misc{rajagopal2020multiprecision,
      title={Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs}, 
      author={Aditya Rajagopal and Diederik Adriaan Vink and Stylianos I. Venieris and Christos-Savvas Bouganis},
      year={2020},
      eprint={2006.09049},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Checkout https://www.imperial.ac.uk/intelligent-digital-systems to see other publications by the Intelligent Digital Systems Lab at Imperial College London.

Installation

git clone https://github.com/ICIdsl/pytorch_training.git
cd pytorch_training
git submodule update --init src/muppet 

The following section defines MuPPET specific config file parameters. config.ini

MuPPET_Hyperparameters

• Run_Muppet : If False, all following parameters are ignored and regular training is performed
• Bit_Width : Bitwidth at which MuPPET training begins. If FP32, set to -1
• Data_Type : One of "DFixed" or "Float". Has to match Bit_Width specified
• Round_Meth : One of "Simple" or "Stochastic"
• Policy_Resolution : Refer to resolution hyperparameter in associated paper
• Policy_Patience : Refer to patience hyperparameter in associated paper
• Fp32_Epochs_Per_Lr : Number of epochs run at each learning rate once in FP32 training
• Prec_Schedule : Precisions to change into at each switch. First precision must match with the value for Bit_Width