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
An embedded programmable processor for compressive sensing applications
|Author:||Safarpour, Mehdi1; Hautala, Ilkka1; Silvén, Olli1|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201901021154
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-01-02
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.
|Pages:||1 - 5|
2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC)30-31 October 2018, Tallinn, Estonia
|Host publication editor:||
IEEE Nordic Circuits and Systems Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The support of the Academy of Finland for the ICONICAL project is gratefully acknowledged.
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