@proceedings {1440953,
	title = {GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators},
	year = {2022},
	address = {Baltimore, Maryland, USA},
	abstract = {This paper presents GoldenEye, a functional simulator with fault injection capabilities for common and emerging numerical formats, implemented for the PyTorch deep learning framework. Gold- enEye provides a unified framework for numerical format evaluation of DNNs, including traditional number systems such as fixed and floating point, as well as recent DNN-inspired formats such as block floating point and AdaptivFloat. Additionally, GoldenEye enables single- and multi- bit flips at various logical and functional points during a value{\textquoteright}s lifetime for resiliency analysis, including for the first time attention to numerical values{\textquoteright} hardware metadata. This paper describes Golden- Eye{\textquoteright}s technical design and implementation which make it an easy-to- use, extensible, versatile, and fast tool for dependability research and future DNN accelerator design. We showcase its utility with three case studies: a unifying platform for number system comparison and eval- uation, a design-space exploration heuristic for data type selection, and fast DNN reliability analysis for different error models. GoldenEye is open-sourced and available at: <a data-url="https://github.com/ma3mool/goldeneye" href="https://github.com/ma3mool/goldeneye" title="">https://github.com/ma3mool/goldeneye</a>.},
	url = {https://doi.org/10.1109/DSN53405.2022.00031},
	author = {Abdulrahman Mahmoud and Tambe, Thierry and Tarek Aloui and David Brooks and Gu-Yeon Wei}
}
