GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators

Abdulrahman Mahmoud, Thierry Tambe, Tarek Aloui, David Brooks, Gu-Yeon Wei

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. GoldenEye 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 evaluation, 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: https://github.com/ma3mool/goldeneye.