Publications by Author: Tarek Aloui

Abdulrahman Mahmoud, Thierry Tambe, Tarek Aloui, David Brooks, and Gu-Yeon Wei. 6/27/2022. “GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators.” 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). Baltimore, Maryland, USA. Publisher's VersionAbstract
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’s lifetime for resiliency analysis, including for the first time attention to numerical values’ hardware metadata. This paper describes Golden- Eye’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: