Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Motivation Sequence alignment is a memory bound computation whose performance in
modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory …

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

To understand and improve DRAM performance, reliability, security, and energy efficiency,
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …

In the last years, advances in genome sequencing technologies have enabled the
proliferation of genomic applications that guide personalized medicine. These applications …

Generating the hash values of short subsequences, called seeds, enables quickly
identifying similarities between genomic sequences by matching seeds with a single lookup …

Reinforcement Learning (RL) is the process by which an agent learns optimal behavior
through interactions with experience datasets, all of which aim to maximize the reward …

Data movement between memory and processors is a major bottleneck in modern
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …

Processing-in-memory (PIM) promises to alleviate the data movement bottleneck in modern
computing systems. However, current real-world PIM systems have the inherent …

High-throughput sequencing (HTS) technologies have revolutionized the field of genomics,
enabling rapid and cost-effective genome analysis for various applications. However, the …