Performance evaluation of vision algorithms on FPGA

Abstract

The modern FPGAs enable system designers to develop high-performance com- puting (HPC) applications with large amount of parallelism. Real-time image processing is such a requirement that demands much more processing power than a conventional processor can deliver. In this research, we implemented software and hardware based architectures on FPGA to achieve real-time image processing. Furthermore, we benchmark and compare our implemented architectures with ex- isting architectures. The operational structures of those systems consist of on-chip processors or custom vision coprocessors implemented in a parallel manner with e cient memory and bus architectures. The performance properties such as the accuracy, throughput and e ciency are measured and presented.// According to results, FPGA implementations are faster than the DSP and GPP implementations for algorithms which can exploit a large amount of parallelism. Our image pre-processing architecture is nearly two times faster than the opti- mized software implementation on an Intel Core 2 Duo GPP. However, because of the higher clock frequency of DSPs/GPPs, the processing speed for sequential computations on on-chip processors in FPGAs is slower than on DSPs/GPPs. These on-chip processors are well suited for multi-processor systems for software level parallelism. 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 e ective for implementing custom parallel architecture to achieve real time image process- ing.