We present a customizable Collective Knowledge workflow to study the execution time vs. accuracy trade-offs for the MobileNets CNN family. We use this workflow to evaluate MobileNets on Arm Cortex CPUs using TensorFlow and Arm Mali GPUs using several versions of the Arm Compute Library. Our optimizations for the Arm Bifrost GPU architecture reduce the execution time by 2--3 times, while lying on a Pareto-optimal frontier. We also highlight the challenge of maintaining the accuracy when deploying CNN models across diverse platforms. We make all the workflow components (models, programs, scripts, etc.) publicly available to encourage further exploration by the community.
Multi-objective autotuning of MobileNets across the full software/hardware stack / Lokhmotov, Anton; Chunosov, Nikolay; Vella, Flavio; Fursin, Grigori. - (2018). (Intervento presentato al convegno 1st on Reproducible Quality-Efficient Systems Tournament on Co-designing Pareto-efficient Deep Learning, ReQuEST@ASPLOS 2018 tenutosi a Williamsburg, US) [10.1145/3229762.3229767].
Multi-objective autotuning of MobileNets across the full software/hardware stack
Vella, Flavio;
2018
Abstract
We present a customizable Collective Knowledge workflow to study the execution time vs. accuracy trade-offs for the MobileNets CNN family. We use this workflow to evaluate MobileNets on Arm Cortex CPUs using TensorFlow and Arm Mali GPUs using several versions of the Arm Compute Library. Our optimizations for the Arm Bifrost GPU architecture reduce the execution time by 2--3 times, while lying on a Pareto-optimal frontier. We also highlight the challenge of maintaining the accuracy when deploying CNN models across diverse platforms. We make all the workflow components (models, programs, scripts, etc.) publicly available to encourage further exploration by the community.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
