Today's high-performance computing (HPC) systems are largely structured based on traditional central processing units (CPUs) with tightly coupled general-purpose graphics processing units (GPUs, which can be considered domain-specific accelerators). GPUs have a different programming model than CPUs and are only efficient in exploiting spatial parallelism...

Today's high-performance computing (HPC) systems are largely structured based on traditional central processing units (CPUs) with tightly coupled general-purpose graphics processing units (GPUs, which can be considered domain-specific accelerators). GPUs have a different programming model than CPUs and are only efficient in exploiting spatial parallelism...

Today's high-performance computing (HPC) systems are largely structured based on traditional central processing units (CPUs) with tightly coupled general-purpose graphics processing units (GPUs, which can be considered domain-specific accelerators). GPUs have a different programming model than CPUs and are only efficient in exploiting spatial parallelism...

­Solving large symmetric sparse linear systems using sparse Cholesky factorization plays a pivotal role in many scientific computing and high-performance computing (HPC) applications. The existing computational solutions to sparse Cholesky factorization based on CPUs and GPUs suffer from very limited performance for to two primary reasons. First,...

Background Convolutional neural networks (CNNs) are a class of deep neural network known for their superior extraction capability of shift/space invariant local features critical for high-level cognition tasks. CNNs are widely applied in image/video processing and computer vision tasks, including image/video recognition, object detection, and semantic...

Background The emergent Internet-of-Things (IoT) data explosion will inevitably exert a tremendous data transmission burden onto the wireless edge networks, which are often low-power wide-area networks (LPWAN) with very limited bandwidth. Thus, improving the bandwidth efficiency of wireless edge networks will be of great importance. In addition, the...

Background In the rapid evolution of deep learning, neural network models have been growing from several to over a hundred layers for handling more complex tasks. Network models are often trained with powerful GPUs in the cloud or on stand-alone servers, and the trained models are then deployed to certain hardware platforms for performing inference....

Background Compressive sensing (CS) is a transformative sampling technique that allows sparse signals to be sampled in compressed form at a rate much lower than the Nyquist rate. In particular, end-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of speed and...

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing Background High-frame-rate cameras are capable of capturing videos at frame rates over 100 frames per second (fps). These devices were originally developed to characterize rapid events. Some high-frame-rate cameras can record high resolution still images...

Background Compressive sensing (CS) is a transformative sampling technique that allows sparse signals to be sampled in compressed form at a rate much lower than the Nyquist rate. However, conventional CS reconstruction techniques, based on either convex optimization or iterative methods, come with three drawbacks in imaging applications: (1) High complexity...