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

Abstract : This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters. This study focuses on porting hyperspectral image processing into many core platforms by optimizing their processing to fulfill real-time constraints, fixed by the image capture rate of the hyperspectral sensor. Real-time is a challenging objective for hyperspectral image processing, as hyperspectral images consist of extremely large volumes of data and this problem is often solved by reducing image size before starting the processing itself. To tackle the challenge, this paper proposes an analysis of the intrinsic parallelism of the different stages of the PCA algorithm with the objective of exploiting the parallelization possibilities offered by an MPPA manycore architecture. Furthermore, the impact on internal communication when increasing the level of parallelism, is also analyzed. 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.
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01622064
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, May 16, 2018 - 10:36:04 AM
Last modification on : Thursday, February 7, 2019 - 5:50:23 PM
Long-term archiving on : Monday, September 24, 2018 - 6:45:29 PM

File

Lazcano et al. - Porting a PCA...
Files produced by the author(s)

Identifiers

Citation

R. Lazcano, D. Madronal, R. Salvador, K. Desnos, Maxime Pelcat, et al.. Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture. Journal of Systems Architecture, Elsevier, 2017, 77, pp.101-111. ⟨10.1016/j.sysarc.2017.05.001⟩. ⟨hal-01622064⟩

Share

Metrics

Record views

322

Files downloads

130