An application specific programmable processor is designed based on the analysis of a set of greedy recovery Compressive Sensing (CS) algorithms. The solution is flexible and customizable for a wide range of problem dimensions, as well as algorithms. The versatility of the approach is demonstrated by implementing Orthogonal Matching Pursuits, Approximate Messaging Passing and Normalized Iterative Hard Thresholding algorithms, all using a high-level language. Transported Triggered Architecture (TTA) framework is employed for the efficient implementation of macro operations shared by the algorithms. The performance of the CS algorithms on ARM Cortex-A15 and NIOS II processors has also been investigated, and empirical comparisons are presented. The flexible hardware design implemented on an FPGA achieves up to 7.80Ksample/s recovery at a power dissipation of 42μJ/sample and beats both ARM and NIOS in total power consumption.
Authors:
Safarpour Mehdi, Hautala Ilkka, Silvén Olli
Publication type:
A4 Article in conference proceedings
Place of publication:
2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC)30-31 October 2018, Tallinn, Estonia
Keywords:
compressive sensing, Embedded Processor, IoT, signal reconstruction
Published:
Full citation:
M. Safarpour, I. Hautala and O. Silvén, “An Embedded Programmable Processor for Compressive Sensing Applications,” 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC), Tallinn, Estonia, 2018, pp. 1-5. doi: 10.1109/NORCHIP.2018.8573494
DOI:
https://doi.org/10.1109/NORCHIP.2018.8573494
Read the publication here:
http://urn.fi/urn:nbn:fi-fe201901021154
