With the rapid growth of classification scale in deep learning systems, the final classification
layer becomes extreme classification with a memory footprint exceeding the main memory …

The recent huge advance of Large Language Models (LLMs) is mainly driven by the
increase in the number of parameters. This has led to substantial memory capacity …

The rapid advancement in genome sequencing technology has led to a significant increase
in the number of genomic reads in recent years. Due to the immense size of reference …

Large-scale machine learning (ML) models rely on extremely large datasets to learn their
exponentially growing number of parameters. While these models achieve unprecedented …

Rapid growth in data size poses increasing computational and memory challenges to data
processing. FPGA accelerators and near-storage processing are promising candidates for …

While (1) serverless computing is emerging as a popular form of cloud execution,
datacenters are going through major changes:(2) storage dissaggregation in the system …

Popular software key-value stores such as LevelDB and RocksDB are often tailored for
efficient writing. Yet, they tend to also perform well on read operations. This is because while …

Feature selection is the data analysis process that selects a smaller and curated subset of
the original dataset by filtering out data (features) which are irrelevant or redundant. The …

High computational capabilities enable modern solid-state drives (SSDs) to be computing
nodes, not just faster storage devices, and the SSD having such capability is generally …

A computational storage device (CSD) using Intel SPDK guarantees low latency and high
throughput. The CSD must aid background tasks for the storage service applications …