Scalable FPGA-based multiprocessor architecture for real-time embedded vision

Real-time image processing demands much more processing power than a conventional processor can deliver. As a result hardware acceleration became necessary to augments processors with application-specific coprocessors. Due to the limited resources on FPGA and nature of some sequential algorithms, it is difficult to depend entirely on slice resources. In this research, we implemented a multiprocessor architecture to support real-time image processing on FPGA. Furthermore, we benchmarked and compared our implemented architectures with their counterparts. The operational structure of multiprocessor architecture consists of on-chip processors implemented in a parallel manner with efficient memory and bus architectures. The performance properties such as accuracy, throughput and efficiency are measured and presented. Multiprocessor systems are effective in software level parallelism on FPGA. Our quad-Microblaze architecture achieved 75-80% performance improvement compared to its single Microblaze counterpart. Moreover, the quad-Microblaze design is faster than the single-powerPC implementation on FPFA. Therefore, multi-processor architecture with customised coprocessors are effective for implementing custom parallel architecture to achieve real-time image processing.