An Embedded Programmable Processor for Compressive Sensing Applications
Mehdi Safarpour,Ilkka Hautala,Olli Silven +2 more
- 01 Oct 2018
- pp 1-5
TL;DR: The flexible hardware design implemented on an FPGA achieves up to 7.80Ksample/s recovery at a power dissipation of 42$\mu$J/sample and beats both ARM and NIOS in total power consumption.
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Abstract: 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$\mu$J/sample and beats both ARM and NIOS in total power consumption.
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Figures
Table 1. Details of designed processor 
Fig. 2. CS based recovery process 
Table 2. FPGA synthesis report for Cyclone IV-EP4CE115F29C7 
Fig. 1. Measurement of the sparse signal 
Fig. 4. Comparison of required time for given reconstruction quality (8% signal occupation) 
Table 3. Performance for different algorithms and platforms
Citations
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TL;DR: This paper proposes an approach to development of dynamically reconfigurable Transport Protocol Controller Unit that allows us to take into account the specific requirements for this unit.
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Distributed Reconstruction of Noisy Pooled Data
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- 14 Apr 2022
TL;DR: This paper presents and analyzes for both error models a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion and pins down the range of error probabilities and distributions for which this algorithm reconstructing the exact initial states with high probability.
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Distributed Reconstruction of Noisy Pooled Data
01 Jul 2022
TL;DR: In this paper , the authors consider two noise models for the pooled data problem, i.e., the noisy channel model and the noisy query model, where each query result is subject to random Gaussian noise, and present a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion.
Використання ядра nios ii у багатоканальному частотомірі на fpga для радіотехнічної системи з частотними сенсорами фізичних величин
TL;DR: In this article , the authors propose Altera Cyclone IV and NIOS II FPGA architectures for the purpose of improving the performance of the Altera Altera Nios II.
1
Design Issues and Challenges of an FPGA-based Orthogonal Matching Pursuit Implementation for Compressive Sensing Reconstruction
Muhammad Muzakkir Mohd Nadzri,Ashfaq Ahmad,Zarina Tukiran +2 more
- 27 Sep 2020
TL;DR: The field-programmable gate array (FPGA) as a viable hardware solution for OMP implementation is reviewed and discussed based on reconstruction time, signal size, number of measurements, sparsity and features.
References
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Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
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Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
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Joel A. Tropp,Anna C. Gilbert +1 more
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TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
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Deanna Needell,Joel A. Tropp +1 more
TL;DR: A new iterative recovery algorithm called CoSaMP is described that delivers the same guarantees as the best optimization-based approaches and offers rigorous bounds on computational cost and storage.