@conference {Wang2020,
	title = {A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms},
	year = {2020},
	publisher = {Third Conference on Machine Learning and Systems (MLSys)},
	organization = {Third Conference on Machine Learning and Systems (MLSys)},
	abstract = {Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware and software specialization to improve performance. To systematically compare deep learning systems, we introduce a methodology comprised of a set of analysis techniques and parameterized end-to-end models for fully connected, convolutional, and recurrent neural networks. This methodology can be applied to analyze various hardware and software systems, and is intended to complement traditional methods. We demonstrate its utility by comparing two generations of specialized platforms (Google{\textquoteright}s Cloud TPU v2/v3), three heterogeneous platforms (Google TPU, Nvidia GPU, and Intel CPU), and specialized software stacks (TensorFlow and CUDA).},
	keywords = {deep learning, DNN benchmarking and characterization, profiling},
	url = {https://proceedings.mlsys.org/papers/2020/12},
	author = {Wang Yu and Gu  Wei and David Brooks}
}
