Open AccessAccelerating Deep Neural Networks implementation: A survey
Author(s) -
Dhouibi Meriam,
Ben Salem Ahmed Karim,
Saidi Afef,
Ben Saoud Slim
Publication year - 2021
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
eISSN - 1751-861X
pISSN - 1751-8601
DOI - 10.1049/cdt2.12016
Subject(s) - computer science , field programmable gate array , software deployment , deep learning , memory footprint , throughput , task (project management) , computer architecture , artificial neural network , field (mathematics) , computation , computer engineering , embedded system , artificial intelligence , systems engineering , software engineering , wireless , engineering , telecommunications , mathematics , algorithm , pure mathematics , operating system
Recently, Deep Learning (DL) applications are getting more and more involved in different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a challenging task considering the massive requirement of computation and storage. Given that the number of operations and parameters increases with the complexity of the model architecture, the performance will strongly depend on the hardware target resources and basically the memory footprint of the accelerator. Recent research studies have discussed the benefit of implementing some complex DL applications based on different models and platforms. However, it is necessary to guarantee the best performance when designing hardware accelerators for DL applications to run at full speed, despite the constraints of low power, high accuracy and throughput. Field Programmable Gate Arrays (FPGAs) are promising platforms for the deployment of large‐scale DNN which seek to reach a balance between the above objectives. Besides, the growing complexity of DL models has made researches think about applying optimization techniques to make them more hardware‐friendly. Herein, DL concept is presented. Then, a detailed description of different optimization techniques used in recent research works is explored. Finally, a survey of research works aiming to accelerate the implementation of DNN models on FPGAs is provided.