A Dynamic Mapping Model for General CNN Accelerator Based on FPGA - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

A Dynamic Mapping Model for General CNN Accelerator Based on FPGA

Résumé

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency.
Fichier principal
Vignette du fichier
511910_1_En_2_Chapter.pdf (537.7 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03768741 , version 1 (04-09-2022)

Licence

Paternité

Identifiants

Citer

Xiaoqiang Zhao, Jingfei Jiang, Zhe Han, Jinwei Xu, Zhiqiang Liu. A Dynamic Mapping Model for General CNN Accelerator Based on FPGA. 17th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2020, Zhengzhou, China. pp.17-29, ⟨10.1007/978-3-030-79478-1_2⟩. ⟨hal-03768741⟩
21 Consultations
4 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More