Multi-objective auto tuning for layer fusion and tensor tiling on multi-level cache hierarchy

A method of performing automatic tuning on a deep learning model includes: utilizing an instruction-based learned cost model to estimate a first type of operational performance metrics based on a tuned configuration of layer fusion and tensor tiling; utilizing statistical data gathered during a comp...

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Main Authors HUNG, SHENG-JE, HSU, JUI-YANG, TSAI, JENIEH, CHAN, CHENG-SHENG, CHEN, YEN-HAO, KUO, BO-YU, HUANG, KAI-LING, TSENG, PING-YUAN, TU, TAO, LI, HUAI-TING
Format Patent
LanguageChinese
English
Published 16.04.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:A method of performing automatic tuning on a deep learning model includes: utilizing an instruction-based learned cost model to estimate a first type of operational performance metrics based on a tuned configuration of layer fusion and tensor tiling; utilizing statistical data gathered during a compilation process of the deep learning model to determine a second type of operational performance metrics based on the tuned configuration of layer fusion and tensor tiling; performing an auto-tuning process to obtain a plurality of optimal configurations based on the first type of platform metrics and the second type of platform metrics; and configure the deep learning model according to one of the plurality of optimal configurations.
Bibliography:Application Number: TW202312138646