Publication

We rst present an application where we helped our astrophysicist collaborators to recover features from articially degraded images with worse seeing and higher noise than the original with a performance which far exceeds simple deconvolution by training a generative adversarial network (GAN) on galaxy images. However, training time is limiting our potential to train on larger data sets. It takes 2 hours to train a GAN using 4105 galaxy images for 20 iterations on an NVIDIA TITAN X GPU. We ask the question: Can we speed up our machine learning training process by reducing the precision of data representation?...

@mastersthesis{abc,
	abstract = {We rst present an application where we helped our astrophysicist collaborators
to recover features from articially degraded images with worse
seeing and higher noise than the original with a performance which far exceeds
simple deconvolution by training a generative adversarial network
(GAN) on galaxy images. However, training time is limiting our potential
to train on larger data sets. It takes 2 hours to train a GAN using
4105 galaxy images for 20 iterations on an NVIDIA TITAN X GPU. We
ask the question: Can we speed up our machine learning training process
by reducing the precision of data representation?...},
	author = {Hantian  Zhang },
	school = {160},
	title = {The ZipML Framework for Training Models with End-to-End Low Precision},
	year = {2017}
}