TY - GEN
T1 - Hardware acceleration of persistent homology computation
AU - Wang, Fan
AU - Deng, Chunhua
AU - Yuan, Bo
AU - Chen, Chao
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - As a powerful tool for topological data analysis, persistent homology captures topological structures of data in a robust manner. Its pertinent information is summarized in a persistence diagram, which records topological structures, as well as their saliency. Recent years have witnessed an increased interest of persistent homology in various domains. In biomedical image analysis, persistent homology has been applied to brain images, neuron images, cardiac images and cancer pathology images. Meanwhile, the computation of persistent homology could be time-consuming due to column operations over a large matrix, called the boundary matrix. This paper seeks to accelerate persistent homology computation with a hardware implementation of the column operations of the boundary matrix. By designing a dedicated hardware to process fast matrix reduction, the proposed hardware accelerator could potentially achieve up to 20k–30k times speed-up.
AB - As a powerful tool for topological data analysis, persistent homology captures topological structures of data in a robust manner. Its pertinent information is summarized in a persistence diagram, which records topological structures, as well as their saliency. Recent years have witnessed an increased interest of persistent homology in various domains. In biomedical image analysis, persistent homology has been applied to brain images, neuron images, cardiac images and cancer pathology images. Meanwhile, the computation of persistent homology could be time-consuming due to column operations over a large matrix, called the boundary matrix. This paper seeks to accelerate persistent homology computation with a hardware implementation of the column operations of the boundary matrix. By designing a dedicated hardware to process fast matrix reduction, the proposed hardware accelerator could potentially achieve up to 20k–30k times speed-up.
KW - Hardware acceleration
KW - Matrix operation
KW - Persistent homology
KW - Topology data analysis
UR - https://www.scopus.com/pages/publications/85076691131
U2 - 10.1007/978-3-030-33642-4_9
DO - 10.1007/978-3-030-33642-4_9
M3 - Conference contribution
AN - SCOPUS:85076691131
SN - 9783030336417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 81
EP - 88
BT - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention - International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhou, Luping
A2 - Heller, Nicholas
A2 - Shi, Yiyu
A2 - Chen, Danny
A2 - Hu, X. Sharon
A2 - Xiao, Yiming
A2 - Sznitman, Raphael
A2 - Cheplygina, Veronika
A2 - Mateus, Diana
A2 - Trucco, Emanuele
A2 - Chabanas, Matthieu
A2 - Rivaz, Hassan
A2 - Reinertsen, Ingerid
PB - Springer
T2 - 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the 1st International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the 2nd International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -