@mastersthesis {Tempe, title = {Learning in Compressed Domains}, volume = {Doctor of Philosophy in Computer Engineering}, year = {2021}, month = {05/2021}, school = {Arizona State University}, address = {Tempe}, abstract = {

A massive volume of data is generated at an unprecedented rate in the information age. The growth of data significantly exceeds the computing and storage capacities of the existing digital infrastructure. In the past decade, many methods are invented for data compression, compressive sensing and reconstruction, and compressed learning (learning directly upon compressed data) to overcome the data-explosion challenge. While prior works are predominantly model-based, focus on small models, and not suitable for task-oriented sensing or hardware acceleration, the number of available models for compression-related tasks has escalated by orders of magnitude in the past decade. Motivated by this significant growth and the success of big data, this thesis proposes to revolutionize both the compressive sensing reconstruction (CSR) and compressed learning (CL) methods from the data-driven perspective.

In this thesis, a series of topics on data-driven CSR are discussed. Individual data-driven models are proposed for the CSR of bio-signals, images, and videos with improved compression ratio and recovery fidelity trade-off. Specifically, a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) is proposed for single-image CSR. LAPRAN progressively reconstructs images following the concept of the Laplacian pyramid through the concatenation of multiple reconstructive adversarial networks (RANs). For the CSR of videos, CSVideoNet is proposed to improve the spatial-temporal resolution of reconstructed videos.

Apart from CSR, data-driven CL is discussed in the thesis. A CL framework is proposed to extract features directly from compressed data for image classification, objection detection, and semantic/instance segmentation. Besides, the spectral bias of neural networks is analyzed from the frequency perspective, leading to a learning based frequency selection method for identifying the trivial frequency components which can be removed without accuracy loss. Compared with the conventional spaitial downsampling approaches, the proposed frequency-domain learning method can achieve higher accuracy with reduced input data size.

The methodologies proposed in this thesis are not restricted to the above-mentioned applications. The thesis also discusses other potential applications and directions for future research.

}, keywords = {psclab}, author = {Kai Xu} } @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} } @article {562, title = {LAPRAN: A SCALABLE LAPLACIAN PYRAMID RECONSTRUCTIVE ADVERSARIAL NETWORK FOR FLEXIBLE COMPRESSIVE SENSING RECONSTRUCTION}, number = {16/745,817}, year = {2020}, month = {07/2020}, keywords = {psclab}, author = {Fengbo Ren and Kai Xu and Zhikang Zhang} } @inproceedings {Seattle, WA, title = {Learning in the Frequency Domain}, year = {2020}, month = {06/2020}, pages = {1740-1749}, address = {Seattle, WA}, abstract = {

Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed with- out accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well- known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.41\% and 0.66\% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1\%. In addition, we observe a 0.8\% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

}, keywords = {psclab}, author = {Kai Xu and Minghai Qin and Fei Sun and Yuhao Wang and Yen-Kuang Chen and Fengbo Ren} } @article {421, title = {Real time end-to-end learning system for a high frame rate video compressive sensing network}, number = {US16/165,568}, year = {2019}, abstract = {

A real time end-to-end learning system for a high frame rate video compressive sensing network is described. The slow reconstruction speed of conventional compressive sensing approaches is overcome by directly modeling an inverse mapping from compressed domain to original domain in a single forward propagation. Through processing massive unlabeled video data such a mapping is learned by a neural network using data-driven methods. Systems and methods according to this disclosure incorporate a multi-rate convolutional neural network (CNN) and a synthesizing recurrent neural network (RNN) to achieve real time compression and reconstruction of video data.

}, keywords = {psclab}, issn = {US20190124346A1}, url = {https://patents.google.com/patent/US20190124346A1/en?inventor=Fengbo+Ren\&oq=inventor:(Fengbo+Ren)}, author = {Fengbo Ren and Kai Xu} } @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} } @article {369, title = {A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks}, journal = {ACM Journal on Emerging Technologies in Computing (JETC) - Special Issue on Frontiers of Hardware and Algorithms for On-chip Learning​}, volume = {14}, year = {2018}, month = {07/2018}, pages = {18.16}, chapter = {18.1}, abstract = {

FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized FPGA accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipelines stages. A key advantage of the FPGA accelerator is that its performance is insensitive to data batch size, while the performance of GPU acceleration varies largely depending on the batch size of the data. Experiment results show that the proposed accelerator architecture for binary CNNs running on a Virtex-7 FPGA is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests in small batch sizes. For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.

}, keywords = {psclab}, doi = {10.1145/3154839}, url = {https://dl.acm.org/citation.cfm?id=3154839}, author = {YIxing Li and Zichuan Liu and Kai Xu and Hao Yu and Fengbo Ren} } @inproceedings {Munich, Germany, title = {LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction}, year = {2018}, month = {09/2018}, pages = {491-507}, address = {Munich, Germany}, abstract = {

This paper addresses the single-image compressive sensing (CS) and reconstruction problem. We propose a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) that enables high- fidelity, flexible and fast CS images reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods. Experimental results on multiple public datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an average 57.93\% and 33.20 \% SSIM improvement compared to model-based and data-driven baselines, respectively.

}, keywords = {psclab}, author = {Kai Xu and Zhikang Zhang and Fengbo Ren} } @inproceedings {2016, title = {A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing}, year = {2017}, month = {03/2017}, pages = {861-865}, address = {New Orleans, LA}, abstract = {

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10x, successfully reduces the isometry constant of the trained sensing matrices by 86\% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.\ 

}, keywords = {psclab}, author = {Xu, Kai and Yixing Li and Ren, Fengbo} } @article {2016, title = {Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {11}, year = {2017}, month = {11/2016}, pages = {255-266}, abstract = {

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6\× and the silicon area by 1.9\× over the data-driven real-valued embedding.

}, keywords = {psclab}, author = {Wang, Yuhao and Li, Xin and Xu, Kai and Ren, Fengbo and Yu, Hao} } @inproceedings {9999, title = {An Energy-Efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-Term Wireless Health Monitoring}, year = {2016}, month = {03/2016}, pages = {804 - 808}, publisher = {IEEE}, address = {Shanghai, China}, abstract = {

Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) together. The learned dictionary carries individual characteristics of the original signal, under which the signal has an even sparser representation compared to pre-determined dictionaries. As a consequence, the compression ratio is effectively improved by 2-4x comparing to prior works. Besides, the proposed framework offloads pre-processing from sensor nodes to the server node prior to dictionary learning, providing further reduction in hardware costs. As it is data driven, the proposed framework has the potential to be used with a wide range of physiological signals.

}, keywords = {psclab}, author = {Kai Xu and YIxing Li and Fengbo Ren} } @article {22, title = {A Square-Root-Free Matrix Decomposition Method for Energy-Efficient Least Square Computation on Embedded Systems}, journal = {IEEE Embedded Systems Letters}, volume = {6}, year = {2014}, month = {Aug.}, pages = {73{\textendash}76}, abstract = {

QR decomposition (QRD) is used to solve least-squares (LS) problems for a wide range of applications. However, traditional QR decomposition methods, such as Gram-Schmidt (GS), require high computational complexity and nonlinear operations to achieve high throughput, limiting their usage on resource-limited platforms. To enable efficient LS computation on embedded systems for real-time applications, this paper presents an alternative decomposition method, called QDRD, which relaxes system requirements while maintaining the same level of performance. Specifically, QDRD eliminates both the square-root operations in the normalization step and the divisions in the subsequent backward substitution. Simulation results show that the accuracy and reliability of factorization matrices can be significantly improved by QDRD, especially when executed on precision-limited platforms. Furthermore, benchmarking results on an embedded platform show that QDRD provides constantly better energy-efficiency and higher throughput than GS-QRD in solving LS problems. Up to 4 and 6.5 times improvement in energy-efficiency and throughput, respectively, can be achieved for small-size problems.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6882128}, author = {Ren, Fengbo and Zhang, Chenxin and Liu, Liang and Xu, Wenyao and Owall, V and Markovi{\'c}, Dejan} } @article {17, title = {Statistical Timing and Power Analysis of VLSI Considering Non-linear Dependence}, journal = {the VLSI Journal Integration}, volume = {47}, year = {2014}, month = {Sep.}, pages = {487{\textendash}498}, abstract = {

Majority of practical multivariate statistical analysis and optimizations model interdependence among random variables in terms of the linear correlation. Though linear correlation is simple to use and evaluate, in several cases non-linear dependence between random variables may be too strong to ignore. In this paper, we propose polynomial correlation coefficients as simple measure of multi-variable non-linear dependence and show that the need for modeling non-linear dependence strongly depends on the end function that is to be evaluated from the random variables. Then, we calculate the errors in estimation resulting from assuming independence of components generated by linear de-correlation techniques, such as PCA and ICA. The experimental results show that the error predicted by our method is within 1\% error compared to the real simulation of statistical timing and leakage analysis. In order to deal with non-linear dependence, we further develop a target-function-driven component analysis algorithm (FCA) to minimize the error caused by ignoring high order dependence. We apply FCA to statistical leakage power analysis and SRAM cell noise margin variation analysis. Experimental results show that the proposed FCA method is more accurate compared to the traditional PCA or ICA.

}, keywords = {psclab}, url = {http://www.sciencedirect.com/science/article/pii/S0167926014000029}, author = {Cheng, Lerong and Xu, Wenyao and Ren, Fengbo and Gong, Fang and Gupta, Puneet and He, Lei} } @inproceedings {29, title = {mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD}, year = {2013}, month = {05/2013}, publisher = {ACM}, abstract = {

COPD (Chronic Obstructive Pulmonary Disease) is a serious lung disease that causes difficulty in breathing. COPD patients require lung function examinations and perform breathing exercises on a regular basis in order to manage and be more aware of their health status. In this paper, we designed and developed a mobile-phone based system for lung function diagnosis, called mCOPD. Besides enabling accurate COPD examinations at home, the mCOPD system also offers a video-game based guidance system for breathing exercises. We evaluated mCOPD in controlled and uncontrolled environments with 40 subjects. The experimental results show that our system is a promising tool for remote medical treatment of COPD.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2504383}, author = {Xu, Wenyao and Huang, Ming-Chun and Liu, Jason J and Ren, Fengbo and Shen, Xinchen and Liu, Xiao and Sarrafzadeh, Majid} } @inproceedings {30, title = {Proper Running Posture Guide: A Wearable Biomechanics Capture System}, year = {2013}, month = {09/2013}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, abstract = {

Running is a popular exercise for all age groups. It helps heart and lung functions, enhances muscle strength and control weight. Nevertheless, excessive fatigue and severe injury resulting from inappropriate running poses might reduce the benefits brought by this exercise and stop people from keeping running regularly. In this paper, we design a system that can monitor the running biomechanics, infer running poses, analyze running patterns, and provide both real-time and off-line feedbacks to reduce unnecessary fatigue and unwanted injuries. Common inappropriate running patterns, over-striding, over-pronating and out-sync, are identified and analyzed with the continuous wearable sensor data streams. Two types of correctional feedbacks are designed to provide users appropriate adjustment guidance: vibrating the areas of user body where improper poses are detected and visualizing running video with sensor waveforms to indicate which inappropriate motions trigger the vibration. With reasonable adjustment, running can be a safe and effective activity for a healthy lifestyle.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2555333}, author = {Zhang, Xiaoyi and Huang, Ming-Chun and Ren, Fengbo and Xu, Wenyao and Guan, Nan and Yi, Wang} } @article {21, title = {Scalable and Parameterised VLSI Architecture for Efficient Sparse Approximation in FPGAs And SoCs}, journal = {IET Electronics Letters}, volume = {49}, year = {2013}, month = {Nov.}, pages = {1440{\textendash}1441}, abstract = {

A parameterised and scalable very large scale integration (VLSI) soft intellectual property (IP) is presented that can be implemented in programmable logic devices, such as field programmable gate arrays (FPGAs) or a system-on-chip design for efficient sparse approximation. The proposed architecture is optimised based on the orthogonal matching pursuit algorithm by both algorithm reformulation and architecture resource sharing techniques. The soft IP core supports a floating-point data format with 10 design parameters, which provides the necessary flexibility for application-specific customisation. The soft IP is evaluated on various FPGA platforms. The evaluation results show that design can achieve up to 30\% higher throughput than the existing solutions while offering a larger dynamic range capability and better design flexibility.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6675715}, author = {Ren, Fengbo and Xu, Wenyao and Markovi{\'c}, Dejan} } @inproceedings {31, title = {See UV on Your Skin: An Ultraviolet Sensing and Visualization System}, year = {2013}, month = {09/2013}, pages = {22-28}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, abstract = {

These days, as the Earth{\textquoteright}s protective ozone layer gets thinner, ultraviolet (UV) radiation threat is growing. In addition, getting tanned as a fashion leads people to wear less clothing, which increases UV intake. Excessive exposure to ultraviolet will lead to sunburn and even skin cancer. Therefore, neither insufficient nor excessive exposure is desirable. Although there are tons of UV meters on the market, a user may have a hard time to understand the unintuitive UV index reading. Thus, there is a potential demand for a portable system which can keep track of daily UV exposure dose, visualize possible sunburned consequences, and provide appropriate skin care recommendations. In this paper, we present a personalized UV monitoring and notification system. This system can continuously track UV exposure by wearable UV sensors. It can also visualize the cumulative UV exposure dose according to a predictive sunburned skin color model. Such an augmented skin color can provide a warning message to indicate the possible result of continuous UV exposure. Compared with other existing systems, our solution not only allows users to monitor their daily UV exposure, but also provides an unobtrusive UV visualization model which effectively warns users to take appropriate actions to avoid potential skin damage. The system has been tested on 9 subjects, and the evaluation feedback indicates that our system is promising for UV monitoring and sunburn prevention.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2555324}, author = {Zhang, Xiaoyi and Xu, Wenyao and Huang, Ming-Chun and Amini, Navid and Ren, Fengbo} } @inproceedings {26, title = {A Single-precision Compressive Sensing Signal Reconstruction Engine on FPGAs}, year = {2013}, month = {09/2013}, pages = {1-4}, publisher = {IEEE}, abstract = {

Compressive sensing (CS) is a promising technology for the low-power and cost-effective data acquisition in wireless healthcare systems. However, its efficient realtime signal reconstruction is still challenging, and there is a clear demand for hardware acceleration. In this paper, we present the first single-precision floating-point CS reconstruction engine implemented a Kintex-7 FPGA using the orthogonal matching pursuit (OMP) algorithm. In order to achieve high performance with maximum hardware utilization, we propose a highly parallel architecture that shares the computing resources among different tasks of OMP by using configurable processing elements (PEs). By fully utilizing the FPGA recourses, our implementation has 128 PEs in parallel and operates at 53.7 MHz. In addition, it can support 2x larger problem size and 10x more sparse coefficients than prior work, which enables higher reconstruction accuracy by adding finer details to the recovered signal. Hardware results from the ECG reconstruction tests show the same level of accuracy as the double-precision C program. Compared to the execution time of a 2.27 GHz CPU, the FPGA reconstruction achieves an average speed-up of 41x.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6645574}, author = {Ren, Fengbo and Dorrace, Richard and Xu, Wenyao and Markovi{\'c}, Dejan} }