Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture

Experimenting with medical images obtained from two different surgical use cases, an average speedup of 20 is achieved. Internal communications are shown to rapidly become the bottleneck that reduces the achievable speedup offered by the PCA parallelization. As a result of this study, PCA processing time is reduced to less than 6 s, a time compatible with the targeted brain surgery application requiring 1 frame-per-minute.