An Embedded FPGA Operating System Optimized for Vision Computing (Abstract Only)

Abstract

Although FPGA's power and performance advantages were recognized widely, designing applications on FPGA-based systems is traditionally a task undertaken by hardware experts. It is significant to allow application-level programmers with less system-level but more algorithm knowledge to realize their applications conveniently on FPGAs. In this paper, an embedded FPGA operating system is proposed to facilitate application-level programmers to use FPGAs. Firstly, it builds specific I/Os and optimizes bus interconnection among I/Os, DDR memory, user IPs etc within the FPGA for vision computing. Secondly, it manages resources of the FPGA such as I/Os, DDR memory, communication etc, frees users from low-level details. Thirdly, it schedules tasks (IPs) executed on the FPGA dynamically in runtime, which makes the FPGA multiplexed when necessary. After porting the FPGA operating system to different FPGA platforms and implementing vision algorithms based on that, it shows the FPGA operating system is able to simplify algorithm development on FPGA platforms and improve portability of user applications. Furthermore, implementation results of several popular vision algorithms show the FPGA operating system is efficient and effective for vision computing. Finally, experimental results shows that for multiple algorithms requiring more FPGA resources, runtime task scheduling of multiple IPs is more efficient than a fixed IP when the SoC of FPGA is considered.