Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

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 …

Recently, artificial intelligence applications have become part of almost all emerging
technologies around us. Neural networks, in particular, have shown significant advantages …

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 …

Active storage devices and in-storage computing are proposed and developed in recent
years to effectively reduce the amount of required data traffic and to improve the overall …

All-flash array (AFA) is a popular approach to aggregate the capacity of multiple solid-state
drives (SSDs) while guaranteeing fault tolerance. Unfortunately, existing AFA engines inflict …

With the explosive growth of data volume and great improvement in flash technologies, SSD-
based In-Storage Computing (ISC) is becoming one of the most important means to …

Metagenomics, the study of the genome sequences of diverse organisms in a common
environment, has led to significant advances in many fields. Since the species present in a …

To satisfy prohibitively massive computational requirements of current deep Convolutional
Neural Networks (CNNs), CNN-specific accelerators are widely deployed in large-scale …

Hybrid transactional/analytical processing (HTAP) would overload database systems. To
alleviate performance interference between transactions and analytics, recent research …