Feasibility study and porting of the damped least square algorithm on FPGA

Modern embedded computing platforms used within Cyber-Physical Systems (CPS) are nowadays leveraging more and more often on heterogeneous computing substrates, such as newest Field Programmable Gate Array (FPGA) devices. Compared to general purpose platforms, which have a fixed datapath, FPGAs provide designers the possibility of customizing part of the computing infrastructure, to better shape the execution on the application needs/features, and offer high efficiency in terms of timing and power performance, while naturally featuring parallelism. In the context of FPGA-based CPSs, this article has a two fold mission. On the one hand, it presents an analysis of the Damped Least Square (DLS) algorithm for a perspective hardware implementation. On the other hand, it describes the implementation of a robotic arm controller based on the DLS to numerically solve Inverse Kinematics problems over a heterogeneous FPGA. Assessments involve a Trossen Robotics WidowX robotic arm controlled by a Digilent ZedBoard provided with a Xilinx Zynq FPGA that computes the Inverse Kinematic

DSE and profiling of multi-context coarse-grained reconfigurable systems

The implementation of multi-context systems over coarse-grained reconfigurable platforms could bring several benefits in terms of efficient resource usage and power management. Nevertheless on-the-fly reconfiguration and mapping are not so straightforward and the optimal configuration of the substrate could be extremely time consuming. In this paper we present an early stage design space exploration methodology intended for dataflow-based design flows where multiple input specifications have to be taken into account. The proposed approach, coupled to the Multi-Dataflow Composer tool, has been exploited to assemble the central reconfigurable computing core of an accelerator for video/image processing