NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks

This repository is the official implementation of NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks.

NITRO-D is a novel training framework for arbitrarily deep integer-only Convolutional Neural Networks (CNNs) that operates entirely in the integer-only domain for both training and inference. NITRO-D utilizes a unique learning algorithm derived from Local Error Signals (LES) and it represents the first work in the literature to enable the training of integer-only deep CNNs without the need to introduce a quantization scheme.

NITRO-D introduces both a novel architecture and a novel integer-only learning algorithm designed to train this architecture exploiting IntegerSGD, an optimizer designed specifically to operate in an integer-only context. Experimental evaluations demonstrate its effectiveness across several state-of-the-art image recognition datasets, highlighting considerable performance improvements from 2.47% to 5.96% over the state-of-the-art.

BibTeX entry:

@misc{pirillo2024nitrodnativeintegeronlytraining,
    title={NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks}, 
    author={Alberto Pirillo and Luca Colombo and Manuel Roveri},
    year={2024},
    eprint={2407.11698},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2407.11698}, 
}

Requirements

NITRO-D was developed and tested using Python 3.11 and CUDA 12.1.

Install the required packages using the following command:

python -m pip install -U setuptools pip
pip install -r requirements.txt

(optional) Install cuTensor and cuDNN for optimal performance:

python -m cupyx.tools.install_library --cuda 12.x --library cutensor
python -m cupyx.tools.install_library --cuda 12.x --library cudnn

(optional) Enable the installed accelerators by setting environment variables. On Linux:

echo 'export CUPY_ACCELERATORS=cutensor,cub' >> ~/.bashrc

Training

We provide a separate notebook to reproduce the results for all the configurations considered in the paper. These notebooks are called train.ipynb and are located in the results directory. Each notebook:

• Imports the required libraries
• Defines the experimental setup
• Loads and pre-processes the dataset
• Instantiates and trains the model N_EXPERIMENTS times with different initializations
• Saves the results of each run in a CSV file
• Saves the model of the first run in the model.pkl file
• Computes the average and standard deviation of the train and test accuracy

We also provide two notebooks in the examples directory, nitro_cnn.ipynb and nitro_mlp.ipynb, that detail the training and evaluation of NITRO-D models.

Evaluation and pre-trained models

The models are automatically evaluated inside of the train.ipnyb notebooks. We also provide an additional eval.ipynb notebook in the examples directory, which shows how to load a pre-trained model and evaluate it on a dataset.

Results

NITRO-D models were trained and evaluated on three different datasets: MNIST, FashionMNIST, and CIFAR-10. The results are summarized in the table below, which reports the average test accuracy and the standard deviation over 10 runs.

Model name	Dataset	NITRO-D	PocketNN	FP LES	FP BP
MLP [784-100-50-10]	MNIST	$97.36 \pm 0.23$	$96.98$	-	$98.00$
MLP [784-200-100-50-10]	FashionMNIST	$88.66 \pm 0.46$	$87.70$	-	$89.79$
MLP [1024-3000-3000-3000-10]	CIFAR-10	$61.03 \pm 0.60$	-	$67.70$	$66.40$
VGG8B	MNIST	$99.45 \pm 0.05$	-	$99.60$	$99.74$
VGG8B	FashionMNIST	$93.66 \pm 0.40$	-	$94.34$	$95.47$
VGG8B	CIFAR-10	$87.96 \pm 0.39$	-	$91.60$	$94.01$
VGG11B	CIFAR-10	$87.39 \pm 0.64$	-	$91.61$	$94.44$

Contributing

NITRO-D is licensed under CC BY-NC-SA 4.0