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Accelerating Deep Neural Networks implementation: A survey

Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud
Advanced Systems Laboratory, Tunisia Polytechnic School, University of Carthage, BP 743 La Marsa, Tunisia
IET Computers & Digital Techniques, Volume 15, Issue 2, Pages 79-96, 2021

@article{dhouibi2021accelerating,

   title={Accelerating Deep Neural Networks implementation: A survey},

   author={Dhouibi, Meriam and Ben Salem, Ahmed Karim and Saidi, Afef and Ben Saoud, Slim},

   journal={IET Computers & Digital Techniques},

   volume={15},

   number={2},

   pages={79–96},

   year={2021},

   publisher={Wiley Online Library}

}

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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.
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