Hardware Acceleration in Genode OS Using Dynamic Partial Reconfiguration

Algorithms with operations on large regular data structures such as image processing can be highly accelerated when executed as hardware tasks in an FPGA fabric. The Dynamic Partial Reconfiguration (DPR) feature of new SRAM-based FPGA families allows a dynamic swapping and replacement of hardware tasks during runtime. Particularly embedded systems with processing chains that change over time or that are too large to be implemented in an FPGA fabric in parallel, benefit from DPR. In this paper we present a complete framework for hardware acceleration using DPR in the microkernel based Genode OS. This makes the DPR feature available not only for the high-performance computing field, but also for safety-critical applications. The new framework is evaluated for an exemplary imaging application running on a Xilinx Zynq-7000 SoC

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201