# Hardware Automated Dataflow Deployment of CNNs Source: [Hardware Automated Dataflow Deployment of CNNs]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:02:11.964Z Authors: Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, Cedric Bourrasset, Jean-Charles Quinton, Fran\\c{c}ois Berry Published: 2017-05-04T09:06:29.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()