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Communication dans un congrès

Benchmarking Quantized Neural Networks on FPGAs with FINN

Quentin Ducasse 1, 2 Pascal Cotret 1, 2 Loïc Lagadec 1, 2 Rob Stewart
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The ever-growing cost of both training and inference for state-of-the-art neural networks has brought literature to look upon ways to cut off resources used with a minimal impact on accuracy. Using lower precision comes at the cost of negligible loss in accuracy. While training neural networks may require a powerful setup, deploying a network must be possible on lowpower and low-resource hardware architectures. Reconfigurable architectures have proven to be more powerful and flexible than GPUs when looking at a specific application. This article aims to assess the impact of mixed-precision when applied to neural networks deployed on FPGAs. While several frameworks exist that create tools to deploy neural networks using reduced-precision, few of them assess the importance of quantization and the framework quality. It is used on top of FINN and Brevitas, two frameworks from Xilinx labs, to assess the impact of quantization on neural networks using 2 to 8 bit precisions and weights with several parallelization configurations. The benchmark set up in this work is available in a public repository (
Type de document :
Communication dans un congrès
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Contributeur : Cotret Pascal <>
Soumis le : lundi 21 décembre 2020 - 17:08:37
Dernière modification le : lundi 11 janvier 2021 - 09:35:29


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  • HAL Id : hal-03085342, version 1


Quentin Ducasse, Pascal Cotret, Loïc Lagadec, Rob Stewart. Benchmarking Quantized Neural Networks on FPGAs with FINN. DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures, Feb 2021, Grenoble, France. ⟨hal-03085342⟩



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