Hardware Automated Datafow Deployment of CNNs

Kamel Abdelouahab (1) , Maxime Pelcat (1, 2) , Jocelyn Sérot (1) , François Berry (1) , Cédric Bourrasset (1) , Jean-Charles Quinton (3)

1 IP - Institut Pascal
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 SVH - Statistique pour le Vivant et l’Homme

Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating. However, such techniques are computationally intensive and involve highly regular parallel computation. CNNs can thus benefit from a significant acceleration in execution time when running on fine grain programmable logic devices. As a consequence, several studies have proposed FPGA-based accelerators for CNNs. However, because of the huge amount of the required hardware resources, none of these studies directly was based on a direct mapping of the CNN computing elements onto the FPGA physical resources. In this work, we demonstrate the feasibility of this so-called direct hardware mapping approach and discuss several associated implementation issues. As a proof of concept, we introduce the haddoc2 open source tool, that is able to automatically transform a CNN description into a platform independent hardware description for FPGA implementation.

Mots clés: Haddoc; DHM; FPGA; CNN; FPGA; DHM; Haddoc

Domaines: Architectures Matérielles [cs.AR]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Kamel Abdelouahab, Maxime Pelcat, Jocelyn Sérot, François Berry, Cédric Bourrasset, et al.. Hardware Automated Datafow Deployment of CNNs. [Technical Report] Institut Pascal, Clermont Ferrand. 2017. ⟨hal-01519524⟩