FASE: A fast, accurate and seamless emulator for custom numerical formats
| Autor: | John Osorio, Adria Armejach, Eric Petit, Greg Henry, Marc Casas |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
BFloat16
FP32 Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC] Low precision Deep learning Codes Binary analysis tool Neural networks (Computer science) Computer networks -- Energy consumption Training Power demand Xarxes neuronals (Informàtica) Emulation Ordinadors Xarxes d' -- Consum d'energia Software DNN Aprenentatge profund |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.1109/ISPASS55109.2022.00017 |
| Popis: | 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. Marc Casas has been partially supported by the Grant RYC2017-23269 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. Adria Armejach is a Serra Hunter Fellow and has been partially supported by the Grant IJCI-2017-33945 funded by MCIN/AEI/ 10.13039/501100011033. John Osorio has been partially supported by the Grant PRE2019- 090406 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. This work has been partially supported by Intel under the BSC-Intel collaboration and European Union’s Horizon 2020 research and innovation programme under grant agreement No 955606 - DEEP-SEA EU project. |
| Databáze: | OpenAIRE |
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