This paper investigates the feasibility of using inexpensive flash memory on new
interconnect technologies such as CXL (Compute Express Link) to overcome the memory …

Bulk bitwise operations, ie, bitwise operations on large bit vectors, are prevalent in a wide
range of important application domains, including databases, graph processing, genome …

Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion
edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are …

The emerging computational storage device offers an opportunity for in-storage computing. It
alleviates the overhead of data movement between the host and the device, and thus …

Building FPGA-based graph learning accelerators is very time-consuming due to the low-
level RTL programming and the complicated design flow of FPGA development. It also …

Graph learning is an emerging technique widely used in diverse applications such as
recommender system and medicine design. Real-world graph learning applications typically …

Data reduction in storage systems is an effective solution to minimize the management cost
of a data center. To maximize data-reduction efficiency, prior works propose post …

The explosive expansion of Deep Neural Networks (DNN) model size expedites the need for
larger memory capacity. This movement is particularly true for models in natural language …

There have been drastic changes in the storage device landscape recently. At the center of
the diverse storage landscape lies the NVMe interface, which allows high-performance and …

Training deep neural network (DNN) models is a resource-intensive, iterative process. For
this reason, nowadays, complex optimizers like Adam are widely adopted as it increases the …