Reconfigurable Hardware-Based Acceleration for Machine Learning and Signal Processing

Hendrik Wöhrle, Frank Kirchner

In: Formal Modeling and Verification of Cyber-Physical Systems: 1st International Summer School on Methods and Tools for the Design of Digital Systems. Graduate School System Design (SyDe-2015) September 9-11 Bremen Germany Seiten 311-313 Lecture Notes in Computer Science (LNCS) Springer Fachmedien Wiesbaden Wiesbaden 9/2015.


Certain application areas of signal processing and machine learning, such as robotics, impose technical limitations on the computing hardware, which make the use of generic processors unfeasible. In this paper we propose a framework for the development of data ow accelerators as a possible solution. The approach is based on model based development and code generation to allow a rapid development of the accelerators and perform a functional verification of the overall system.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence