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Software managed byte-addressable hybrid memory systems consisting of DRAMs and NVMMs offer a lot of flexibility to design efficient large scale data processing applications. Operating systems (OS) play an important role in enabling the…
In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated near a union of low-dimensional subspaces. The goal of SCMD…
Cyber-Physical Systems (CPS) revolutionize various application domains with integration and interoperability of networking, computing systems, and mechanical devices. Due to its scale and variety, CPS faces a number of challenges and opens…
In recent years, the increasing complexity in scientific simulations and emerging demands for training heavy artificial intelligence models require massive and fast data accesses, which urges high-performance computing (HPC) platforms to…
To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding…
Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness. Usually cloud storage system provides users to efficient storage…
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing…
The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure.…
Exa-scale simulations are on the horizon but almost no new design for the output has been proposed in recent years. In simulations using individual time steps, the traditional snapshots are over resolving particles/cells with large time…
The evolution from traditional IP-based networking to Named Data Networking (NDN) represents a paradigm shift to address the inherent limitations of current network architectures, such as scalability, mobility, and efficient data…
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes…
Robot manipulation in unstructured environments requires efficient and reliable Swept Volume Collision Detection (SVCD) for safe motion planning. Traditional discrete methods potentially miss collisions between these points, whereas SVCD…
Deep neural networks (DNN) use a wide range of network topologies to achieve high accuracy within diverse applications. This model diversity makes it impossible to identify a single "dataflow" (execution schedule) to perform optimally…
The increasing demand for SSDs coupled with scaling difficulties has left manufacturers scrambling for newer SSD interfaces which promise better performance and durability. While these interfaces reduce the rigidity of traditional…
Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an…
The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a…
Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage processing (ISP), processing using DRAM in the SSD (PuD-SSD), and…
Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access…
Persistent or Non Volatile Memory (PMEM or NVM) has recently become commercially available under several configurations with different purposes and goals. Despite the attention to the topic, we are not aware of a comprehensive empirical…
As spatial and temporal resolutions of scientific instruments improve, the explosion in the volume of data produced is becoming a key challenge. It can be a critical bottleneck for integration between scientific instruments at the edge and…