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.
