FASE: A Fast, Accurate and Seamless Emulator for Custom Numerical Formats
John Osorio, Adrià Armejach, Eric Petit, Greg Henry, Marc Casas · 2022
Deep Neural Networks (DNNs) have become ubiquitous in a wide range of application domains. Despite their success, training DNNs is an expensive task that has motivated the use of reduced numerical precision formats to improve performance and reduce power consumption. Emulation techniques are a good fit to understand the properties of new numerical formats on a particular workload. However, current SoA techniques are not able to perform these tasks quickly and accurately on a wide variety of workloads.We propose FASE, a Fast, Accurate, and Seamless Emulator that leverages dynamic binary translation to enable emulation of custom numerical formats. FASE is fast: allowing emulation of large unmodified workloads; accurate: emulating at the instruction operand level; and seamless: as it does not require any code modifications and works on any application or DNN framework without any language, compiler, or source code access restrictions.