Signal Processing and Machine Learning on Reconfigurable Hardware

Hendrik Wöhrle; Johannes Teiwes

DFKI GmbH, DFKI Documents ( D), Vol. 14-05, ISBN ISSN 0946-0098, Selbstverlag, 7/2014.


In this poster, the framework reSPACE for signal processing and machine learning on reconfigurable hardwareis introduced. It allows to rapidly develop application-specific, FPGA-based hardware accelerators to Speed up certain computational intensive data processing tasks. The underlying computational model is the static heterogeneous synchronous dataflow computing paradigm. In order to make the hardware accelerators accessible, it utilizes various model-based software Generation techniques to automatically generate device drivers and test facilities for simulation- and hardware-based verification.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence