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Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Maribel I. Castillo, Juan Carlos Fernandez, Francisco D. Igual, Antonio Plaza, Enrique S. Quintana-Orti, Alfredo Remon
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures.  ...  We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if  ...  We thank TI for the donation of the DSP processor used in the experimental section.  ... 
doi:10.1109/jstars.2013.2266927 fatcat:wku6xdgv6vcgpgkrlchrd7p6su

Hardware implementation of machine vision systems: image and video processing

Guillermo Botella, Carlos García, Uwe Meyer-Bäse
2013 EURASIP Journal on Advances in Signal Processing  
This contribution focuses on different topics covered by the special issue titled 'Hardware Implementation of Machine vision Systems' including FPGAs, GPUS, embedded systems, multicore implementations  ...  for image analysis such as edge detection, segmentation, pattern recognition and object recognition/interpretation, image enhancement/restoration, image/video compression, image similarity and retrieval  ...  Acknowledgements The guest editors would like to thank the authors for their cooperation, interest, and their top-quality contributions presented in this special issue.  ... 
doi:10.1186/1687-6180-2013-152 fatcat:5vhtqrnqpjbtnafyz733y6xv24

Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik (+35 others)
2022 Frontiers in Big Data  
The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms.  ...  FPGA Programming: FPGA are configurable integrated circuits that provide a good trade-off in terms of performance, power consumption, and flexibility with respect to other hardware paradigms.  ... 
doi:10.3389/fdata.2022.787421 pmid:35496379 pmcid:PMC9041419 fatcat:5w2exf7vvrfvnhln7nj5uppjga