  This paper presents a complete video fusion system with hardware acceleration
and investigates the energy trade-offs between computing in the CPU or the FPGA
device. The video fusion application is based on the Dual-Tree Complex Wavelet
Transforms (DT-CWT). In this work the transforms are mapped to a hardware
accelerator using high-level synthesis tools for the FPGA and also vectorized
code for the single instruction multiple data (SIMD) engine available in the
CPU. The accelerated system reduces computation time and energy by a factor of
2. Moreover, the results show a key finding that the FPGA is not always the
best choice for acceleration, and the SIMD engine should be selected when the
wavelet decomposition reduces the frame size below a certain threshold. This
dependency on workload size means that an adaptive system that intelligently
selects between the SIMD engine and the FPGA achieves the most energy and
performance efficiency point.
