Efficient reconfigurable regions management method for adaptive and dynamic FPGA based systems

TL;DR

This paper presents a novel method for managing reconfigurable regions in FPGA-based adaptive systems, optimizing dynamic partial reconfiguration for video processing applications with significant improvements in configuration time, memory, and resource utilization.

Contribution

It introduces a design method for efficiently managing variable-size hardware modules in DPR FPGAs, enhancing flexibility and performance in adaptive systems.

Findings

Configuration time reduced by up to 66%

Memory storage reduced by up to 87%

Hardware resource utilization up to 90%

Abstract

Adaptive systems based on field programmable gate array (FPGA) architectures can greatly benefi t fro m th e high degree of flexibility offered by dynamic partial reconfiguration (DPR). By using this technique, hardware tasks can be loaded and reloaded on demand depending on the system requirements. In this paper, we propose to use the DPR for dynamic and adaptive implementation of a video cut detection application based on the MPEG-7 color structure descriptor (CSD). In the proposed implementation, different scenarios have been tested. Depending on the application and the system requirements, the CSD module can be loaded at any time with variable module size (corresponding to different version of the CSD) and allocated in different possible reconfigurable regions. Such implementation entails many problems related to communication, relocation and reconfigurable region management. We will demonstrate how we have made this implementation successful through the use of an appropriate design method. This method was proposed to support the management of variable-size hardware tasks on DPR FPGAs based adaptive systems. It permits to efficiently handle the reconfigurable area and to relocate the reconfigurable modules in different possible regions. The implementation results for the considered application show an important optimization in terms of configuration time (until 66 %) and memory storage (until 87 %) and an efficient hardware resources utilization rate (until 90%).