Co-Designing a 3D Transformation Accelerator for Versal-Based Image Registration

Rigid image registration is pivotal in modern imaging for correcting distortions of images acquired with different modalities or at different time instants. Literature accelerates its main compute-intensive steps, image transformation and similarity metric computation, through GPUs or FPGAs to meet performance-efficiency constraints. However, GPUs lack energy efficiency while FPGAs lack performance for image transformation. Therefore, we adopt a single heterogeneous system through PEGASO, a methodology and its implementation to co-design the image transformation algorithm on Versal system. We maximize performance by combining custom data layout and hardware optimizations, attaining a 19x speedup over the best GPU-based transformation accelerator. When integrated with FPGA-based similarity metric, PEGASO achieves 82.73x and 20.73x speedup against FPGA- and GPU-based solutions while improving the corresponding energy efficiency of 318.18x and 52.62x.