An Embedded Programmable Processor for Compressive Sensing Applications

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 b...

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Veröffentlicht in:2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC) S. 1 - 5
Hauptverfasser: Safarpour, Mehdi, Hautala, Ilkka, Silven, Olli
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2018
Schlagworte:
Approximation algorithms
Compressed sensing
Compressive Sensing
Embedded Processor
IoT
Matching pursuit algorithms
Power demand
Registers
Signal Reconstruction
Software algorithms
Sparse matrices
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Zusammenfassung: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 42\muJ/sample and beats both ARM and NIOS in total power consumption.
DOI:10.1109/NORCHIP.2018.8573494