TY - JOUR
T1 - Processing-in-memory for genomics workloads
AU - Simon, William Andrew
AU - Yavits, Leonid
AU - Koliogeorgi, Konstantina
AU - Falevoz, Yann
AU - Shibuya, Yoshihiro
AU - Lavenier, Dominique
AU - Boybat, Irem
AU - Zambaku, Klea
AU - Sahin, Berkan
AU - Sadrosadati, Mohammad
AU - Mutlu, Onur
AU - Sebastian, Abu
AU - Chikhi, Rayan
AU - Consortium, The Bio PIM
AU - Alkan, Can
N1 - Publisher Copyright:
© 1981-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the main workforce for 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 started the BioPIM Project to leverage the 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 for the highest cost, energy, and time savings benefit.
AB - Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the main workforce for 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 started the BioPIM Project to leverage the 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 for the highest cost, energy, and time savings benefit.
UR - https://www.scopus.com/pages/publications/105029982460
U2 - 10.1109/MM.2026.3662105
DO - 10.1109/MM.2026.3662105
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AN - SCOPUS:105029982460
SN - 0272-1732
JO - IEEE Micro
JF - IEEE Micro
ER -