Reconfigurable Dataflow Hardware Accelerators for Machine Learning and Robotics
Hendrik Wöhrle, Johannes Teiwes, Mario Michael Krell, Anett Seeland, Elsa Andrea Kirchner, Frank Kirchner
In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, (ECML PKDD-2014), 15.9.-19.9.2014, Nancy, Springer, pages 129-138, Sep/2014.
The trend in robotics is to develop increasingly more intelligent systems. This leads directly to a considerable demand for more and more computational power. However, there are several technical limitations, like restrictions regarding power consumption and physical size, that make the use of powerful generic processors unfeasible. One possibility to overcome this problem is the usage of specialized hardware accelerators, which are designed for typical tasks in robotics and machine learning. In this paper, we propose an approach for the rapid development of hardware accelerators that are based on the heterogeneous data how computing paradigm. The developed techniques are collected in a framework to provide a simple access to them. We discuss different application areas and show first results in the field of biosignal analysis that can be used for rehabilitation robotics.
Robotics, Embedded Systems, FPGA, Hardware Acceleration, Dataflow