CAEMO - A Flexible and scalable high performance matrix algebra coprocessor for embedded reconfigurable computing systems

Hendrik Wöhrle, Frank Kirchner

In: Microprocessors and Microsystems Seiten 47-63 Elsevier 2/2018.


Many applications in mobile and embedded systems like signal processing, ma- chine learning, kinematics, dynamics, and control depend on computationally expensive matrix operations. However, such systems underlie tight constraints regarding power consumption and physical space, which prohibits the usage of powerful multicore systems. In this paper, we propose a novel scalable and power-efficient architecture for matrix algebra in FPGA-based Systems-on-Chip. The architecture is based on a linear systolic array and has been developed with a focus on exibility in order to be adapted to different applications. We eval- uate the performance, resource utilization and power consumption of different configurations and show that it provides significant speed-ups over a mobile processor and is significantly more power efficient than a standard PC.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence