@inproceedings {Barcelona, Spain, title = {CRA: A Generic Compression Ratio Adaptor for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks}, year = {2020}, month = {05/2020}, pages = {1740-1749}, address = {Barcelona, Spain}, abstract = {

End-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy. However, due to their end-to-end nature, existing EDCSR frameworks can not adapt to a variable compression ratio (CR). For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. This paper presents a generic compression ratio adapter (CRA) framework that addresses the variable compression ratio (CR) problem for existing EDCSR frameworks with no modification to given reconstruction models nor enormous rounds of training needed. CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of the acquired measurements. Subsequently, CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with resensed initial estimate. Our experiments based on two public image datasets (CIFAR10 and Set5) show that CRA provides an average of 13.02 dB and 5.38 dB PSNR improvement across the CRs from 5 to 30 over a naive zero-padding approach and the AdaptiveNN approach(a prior work), respectively. CRA addresses the fixed-CR limitation of existing EDCSR frameworks and makes them suitable for resource-constrained compressive sensing applications.

}, keywords = {psclab}, author = {Zhikang Zhang and Kai Xu and Fengbo Ren} } @inproceedings {270, title = {CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing}, year = {2018}, month = {03/2018}, abstract = {

This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS). Unlike prior works that perform reconstruction using iterative optimization-based approaches, we propose a non-iterative model, named \“CSVideoNet\”. CSVideoNet directly learns the inverse mapping of CS and reconstructs the original input in a single forward propagation. To overcome the limitations of existing CS cameras, we propose a multi-rate CNN and a synthesizing RNN to improve the trade-o‚ between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. The experiment results demonstrate that CSVideoNet\ significantly\ outperforms the state-of-the-art approaches. With no pre/post-processing, we achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on a Titan X GPU. Due to the feedforward and high-data-concurrency natures of CSVideoNet, it can take advantage of GPU acceleration to achieve three orders of magnitude speed-up over conventional iterative-based approaches. We share the source code at https://github.com/PSCLab-ASU/CSVideoNet.

}, keywords = {psclab}, doi = {10.1109/WACV.2018.00187}, author = {Kai Xu and Fengbo Ren} } @inproceedings {104, title = {A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data}, year = {2016}, month = {04/2016}, pages = {19-25}, abstract = {

Online testing vehicle is required for 3D TSV pre-bond and post-bond testing due to high probability of TSV failures. It has become a challenge to deal with large sets of generated testing data with limited probing when transmitting the data out. In this paper, a lossless compressive-sensing based testing vehicle is developed for online testing of TSVs. By exploring sparsity of the testing data under constraint of failure bound of TSV, sparse-representation based encoding can be deployed by XOR and AND network on chip to deal with large volume of testing data. Experimental results (with benchmarks) have shown that 89.70\% pre-bond data compression rate can be achieved under 0.5\% probability of failures; and 88.18\% post-bond data compression rate can be achieved with 5\% probability of failures.

}, keywords = {psclab}, author = {Hantao Huang and Hao Yu and Cheng Zhuo and Fengbo Ren} } @article {2015, title = {A Configurable 12{\textendash}237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals}, journal = {IEEE Journal of Solid-State Circuits}, volume = {51}, year = {2016}, month = {10/2015}, pages = {68-78}, abstract = {

Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12\—237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming \<1\%of a smartphone\’s power budget. Such energy-efficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=\&arnumber=7299281\&url=http\%3A\%2F\%2Fieeexplore.ieee.org\%2Fxpls\%2Fabs_all.jsp\%3Farnumber\%3D7299281}, author = {Ren, Fengbo and Markovi{\'c}, Dejan} } @inproceedings {27, title = {A Configurable 12-to-237KS/s 12.8 mW Sparse-approximation Engine for Mobile ExG Data Aggregation}, year = {2015}, month = {Feb.}, pages = {68-78}, publisher = {IEEE}, abstract = {

Compressive sensing (CS) is a promising solution for low-power on-body sensors for 24/7 wireless health monitoring. In such an application, a mobile data aggregator performing real-time signal reconstruction is desired for timely prediction and proactive prevention. However, CS reconstruction requires solving a sparse approximation (SA) problem. Its high computational complexity makes software solvers, consuming 2-50W on CPUs, very energy inefficient for real-time processing. This paper presents a configurable SA engine in a 40nm CMOS technology for energy-efficient mobile data aggregation from compressively sampled biomedicai signals. Using configurable architecture, a 100\% utilization of computing resources is achieved. An efficient data-shuffling scheme is implemented to reduce memory leakage by 40\%. At the minimum-energy point (MEP), the SA engine achieves a real-time throughput for reconstructing 61-to-237 channels of biomedicai signals simultaneously with \<;1\% of a mobile device\&$\#$39;s 2W power budget, which is 76-350\× more energy-efficient than prior hardware designs.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7063062}, author = {Ren, Fengbo and Markovi{\'c}, Dejan} }