Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized, and Participatory (termed 'P4') medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming substantial energy, and wasting valuable time. Therefore, there is a need for fast, energy-efficient, and cost-efficient technologies that enable genomics research without requiring data centers and cloud platforms. We recently launched the BioPIM Project to leverage emerging processing-in-memory (PIM) technologies to enable energy- and cost-efficient analysis of bioinformatics workloads. The BioPIM Project focuses on co-designing algorithms and data structures commonly used in genomics with several PIM architectures to achieve the highest cost, energy, and time savings.
@article{arxiv.2506.00597,
title = {Processing-in-memory for genomics workloads},
author = {William Andrew Simon and Leonid Yavits and Konstantina Koliogeorgi and Yann Falevoz and Yoshihiro Shibuya and Dominique Lavenier and Irem Boybat and Klea Zambaku and Berkan Şahin and Mohammad Sadrosadati and Onur Mutlu and Abu Sebastian and Rayan Chikhi and The BioPIM Consortium and Can Alkan},
journal= {arXiv preprint arXiv:2506.00597},
year = {2026}
}