A Single-Precision Compressive Sensing Signal Reconstruction Engine on FPGAs (original) (raw)
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Compressive sampling hardware reconstruction
2010
Compressive Sampling reconstruction techniques require computationally intensive algorithms, often using L 1 optimization to reconstruct a signal that was originally sampled at a sub-Nyquist rate. In this work we present a VLSI implementation of a computationally efficient algorithm named Orthogonal Matching Pursuit. We further optimize the algorithm to meet typical hardware constraints and describe the different block units of our design. We synthesize our design for the Xilinx Virtex 5 FPGA and give timing and area results. We summarize our work with a short discussion of the possible uses for our system.
IET Circuits, Devices & Systems, 2021
Compressed sensing-based radio frequency signal acquisition systems call for higher reconstruction speed and low dynamic power. In this study, a novel low power fast orthogonal matching pursuit (LPF-OMP) algorithm is proposed for faster reconstruction of sparse signals from their compressively sensed samples and the reconstruction circuit consumes very low dynamic power. The searching time to find the best column is reduced by reducing the number of columns to be searched in successive iterations. A novel architecture of the proposed LPF-OMP algorithm is also presented here. The proposed architecture is implemented on field programmable gate array for demonstrating the performance enhancement. Computation of pseudoinverse in OMP is avoided to save time and storage requirement to store the pseudoinverse matrix. The proposed design incorporates a novel strategy to stop the algorithm without consuming any extra circuitry. A case study is carried out to reconstruct the RADAR test pulses. The design is implemented for K = 256, N = 1024 using XILINX Virtex6 device and supports maximum of K/4 iterations. The proposed design is faster, hardware efficient and consumes very less dynamic power than the previous implementations of OMP. In addition, the proposed implementation proves to be efficient in reconstructing low sparse signals. 1 | INTRODUCTION High-frequency radio frequency (RF) signals, such as RADAR pulses, are sparse in nature in the transform domain. Exploiting this sparsity property, modern signal measurement systems use compressed sensing (CS) [1,2] in place of other existing sampling techniques [3] to acquire RF signals. CSbased acquisition systems can work with low speed analog-todigital converters due to sampling at sub-Nyquist rate [4]. In CS-based sampling paradigm, random measurements are taken from the signal and then the original signal is recovered from the measurement samples using signal recovery algorithms. Orthogonal matching pursuit (OMP) [5,6] is a well known recovery algorithm. Unlike the other greedy pursuit algorithms, OMP provides better performance with moderate computational complexity. OMP estimates a sparse signal by executing two steps in every iteration, viz., perform the atom searching (AS) and solve a least squares (LS) problem. In AS step, OMP identifies an atom or a column of the sampling matrix which gives maximum correlation with the current residual. Subsequently the signal is estimated by solving an LS problem. The timing complexity of the AS step is very high as it is a linear function of the signal sparsity and the number of samples. Many techniques are reported in literature to reduce the timing complexity of AS step. In [7] authors applied clustering algorithms to group the similar columns and reported a tree-based pursuit algorithm. But such algorithm has no reports of implementation. Researchers reported parallel selection of multiple columns to address the timing complexity problem in [8,9]. Multiple selection of columns reduces the timing complexity but with greater chances of choosing wrong columns. Many implementations are reported based on either field programmable gate arrays (FPGAs) or application-specific integrated circuits. The LS problem is solved in different ways in current research works. The implementations of OMP reported in [9-15] used modified Cholesky factorization [16] to solve the LS problem. The LS problem is solved by lowerupper decomposition [16] in [17]. QR decomposition [16] is another powerful matrix factorization technique which is used This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
FPGA implementation of LSD-OMP for real-time ECG signal reconstruction
2021
Compressed sensing is widely used to compress electrocardiogram (ECG) signals, but the major challenges of the compressed sensing algorithms are their highly complex signal reconstruction processes. In this paper, a reconfigurable high-speed and low-power field-programmable gate array (FPGA) implementation of the least support denoising-orthogonal matching pursuit (LSD-OMP) algorithm for the real-time reconstruction of the ECG signals is presented. The contribution of this study is twofold: Firstly, LSD-OMP can pick more than one element at each iteration and reconstruct the sparse signal using less number of iterations as compared to the standard OMP algorithms. Latency of the proposed design is therefore reduced by exploiting the multiple index selection feature of LSD-OMP. Secondly, the proposed architecture is the first reconfigurable LSD-OMP reconstruction architecture which can take different signal sizes and different sparsity levels. The proposed design also includes an efficient inverse wavelet transform (IWT) module to convert the reconstructed signal back into the time-domain. Together with the overhead of the IWT module, the proposed design demonstrates faster execution times while consuming lower power than the existing FPGA implementations; therefore, it can be utilized in wireless body area networks as a back-end unit to reconstruct the compressed ECG signals.
IEEE Access, 2018
Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems. INDEX TERMS Connected health, compressed sensing, energy efficiency, heterogeneous multicore platforms, internet of things, mobile real-time health monitoring, multicore processing, remote monitoring, wearable sensors.
Reconstruction of Compressive Sensing Signal Using Orthogonal Matching Pursuit Algorithm
This paper represents the reconstruction of sampled signal in CS by using OMP algorithm. We have used the concept of compressive sensing for sub Nyquist sampling of sparse signal. Compressive sensing reconstruction methods have complex algorithms of l1 optimisation to reconstruct a signal sampled at sub nyquist rate. But out of those algorithm OMP algorithm is fast and computationally efficient. To prove the concept of CS implementation, we have simulated OMP algorithm for recovery of sparse signal of length 256 with sparsity 8.
Sparsity adaptive matching pursuit algorithm for practical compressed sensing
2008
This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing (CS), called the sparsity adaptive matching pursuit (SAMP). Compared with other state-of-the-art greedy algorithms, the most innovative feature of the SAMP is its capability of signal reconstruction without prior information of the sparsity. This makes it a promising candidate for many practical applications when the number of non-zero (significant) coefficients of a signal is not available. The proposed algorithm adopts a similar flavor of the EM algorithm, which alternatively estimates the sparsity and the true support set of the target signals. In fact, SAMP provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases. Such a connection also gives us an intuitive justification of trade-offs between computational complexity and reconstruction performance. While the SAMP offers a comparably theoretical guarantees as the best optimization-based approach, simulation results show that it outperforms many existing iterative algorithms, especially for compressible signals.
2011
Modern multicore architectures require adapted, parallel algorithms and implementation strategies for many applications. As a non-trivial example we chose in this paper a patch-based sparse coding algorithm called Orthogonal Matching Pursuit (OMP) and discuss parallelization and implementation strategies on current hardware. The OMP algorithm is used in imaging and involves heavy computations on many small blocks of pixels called patches. From a global view the patches within the image can be processed completely in parallel but within one patch the algorithm is hard to parallelize. We compare the performance on the Cell Broadband Engine Architecture (CBEA), different GPUs, and current multicore CPUs.
Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device
Microprocessors and Microsystems, 2019
In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gatewaycentric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM's big.little TM multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.