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.
@article{arxiv.1705.04543,
title = {Hardware Automated Dataflow Deployment of CNNs},
author = {Kamel Abdelouahab and Maxime Pelcat and Jocelyn Serot and Cedric Bourrasset and Jean-Charles Quinton and François Berry},
journal= {arXiv preprint arXiv:1705.04543},
year = {2018}
}