pith. sign in

arxiv: 2606.22685 · v1 · pith:QC5EI4FPnew · submitted 2026-06-21 · 💻 cs.AR · cs.DC· q-bio.OT

Architecture for Health Initiative (Arch4Health): Computational Challenges in Health-Related Applications and the Role of Computer Architecture in Addressing Them

Nika Mansouri Ghiasi , Konstantina Koliogeorgi , Onur Mutlu This is my paper

Pith reviewed 2026-06-26 09:18 UTC · model grok-4.3

classification 💻 cs.AR cs.DCq-bio.OT
keywords computer architecturehealthcare applicationsbiological datacomputational challengesenergy efficiencydata privacysystem optimizationbiotechnology
0
0 comments X

The pith

Optimizing computing systems is essential to enable high-performance, energy-efficient, private, and secure analysis of large-scale biological data for healthcare advances.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Recent biotechnological advances produce high-throughput, low-cost biological data that could transform healthcare, but conventional computers struggle to process it at the required scale while meeting energy, scalability, privacy, and security demands. The paper introduces the Arch4Health initiative to identify key computational challenges in health and life science applications and to examine how computer architects can address them through system optimizations. A sympathetic reader would care because unoptimized computing blocks the broad adoption of these biotech opportunities. The initiative outlines motivations, vision, goals, related topics, workshops, and future directions to bridge this gap.

Core claim

The central claim is that recent biotech advances create opportunities for healthcare progress through large-scale biological data, yet conventional computing systems cannot sustain the data generation rates or satisfy constraints on energy efficiency, scalability, privacy, and security; therefore, the Arch4Health initiative is needed to pinpoint these challenges in health-related applications and to explore computer architecture solutions that deliver high-performance, energy-efficient, low-cost, private, and secure analysis.

What carries the argument

The Arch4Health initiative, which identifies computational challenges in health applications and explores computer architecture advances to enable optimized biological data analysis.

If this is right

  • Biological data analysis becomes feasible at the scale and speed of modern biotech generation.
  • Healthcare applications gain reliable high-performance processing without prohibitive energy or cost overheads.
  • Privacy and security requirements are met through architecture-level designs rather than software workarounds.
  • Scalable systems emerge that support wider adoption of data-driven medical and life-science advances.
  • Computer architects gain a structured set of targets for hardware and system innovations in the health domain.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Specialized hardware accelerators for genomics or proteomics pipelines could follow directly from the identified challenges.
  • Similar architecture-driven approaches might apply to other high-volume data domains such as real-time medical imaging or population-scale epidemiology.
  • Workshops organized under the initiative could produce benchmark suites that quantify the gap between current systems and required performance.
  • Integration with existing data-center and edge-computing trends would test whether the proposed optimizations remain effective outside controlled lab settings.

Load-bearing premise

Conventional computing systems cannot keep up with the high-throughput rate at which biological data is generated and face additional constraints related to energy efficiency, scalability, privacy, and security.

What would settle it

A demonstration that unmodified conventional computing systems can process current and projected volumes of biological data at required speeds while satisfying energy, privacy, and security constraints without targeted architectural changes.

read the original abstract

Recent biotechnological advances enable high-throughput, low-cost, and accurate biological data generation. This wealth of data enables unique opportunities for advancing healthcare. Despite these opportunities, efficiently analyzing large-scale biological data poses significant challenges for conventional computing systems. These systems often cannot keep up with the high-throughput rate at which data is generated, and they face additional constraints related to energy efficiency, scalability, privacy, and security. Therefore, to facilitate the wide adoption of recent advances in healthcare, there is a need to optimize the computing systems to enable high-performance, energy-efficient, low-cost, private, and secure analysis of biological data. We introduce the Architecture for Health (Arch4Health) initiative, which aims to (i) identify and analyze key computational challenges in current and future health- and life science-related applications and (ii) explore how computer architects and computing system designers can advance healthcare by addressing these challenges. In this short paper, we first present the motivations behind the Arch4Health initiative and, second, elaborate on its vision and goals, related topics, Arch4Health workshops, and future outlooks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper introduces the Architecture for Health (Arch4Health) initiative, motivated by biotechnological advances that enable high-throughput biological data generation. It argues that conventional computing systems cannot keep up with data rates while satisfying constraints on energy efficiency, scalability, privacy, and security, and therefore calls for optimized architectures to support high-performance, efficient, private, and secure analysis. The manuscript outlines the initiative's two aims—identifying computational challenges in health applications and exploring computer-architecture solutions—while describing its vision, goals, related topics, workshops, and future outlooks.

Significance. If pursued, the initiative could usefully focus computer-architecture research on an important application domain. The paper's explicit framing of workshops supplies a concrete mechanism for community building; this is a strength for a short position piece.

minor comments (2)
  1. [Abstract] Abstract: the assertion that conventional systems 'often cannot keep up with the high-throughput rate' is presented without any quantitative illustration or reference; even a single cited data-rate example would make the premise more concrete for readers.
  2. [Vision and Goals] Vision and Goals section: the elaboration on 'related topics' would benefit from naming two or three concrete computational challenges (e.g., read-mapping throughput or variant-calling memory footprint) to illustrate the kinds of problems the initiative intends to address.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the Arch4Health initiative, the recognition of its potential to focus computer-architecture research on an important domain, and the recommendation for minor revision. The report contains no specific major comments to address.

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a short position/vision paper announcing the Arch4Health initiative. It contains no equations, derivations, fitted parameters, predictions, or technical claims that could reduce to inputs by construction. Motivations (e.g., conventional systems cannot keep up with data throughput) are presented as premises, not derived results. No self-citations function as load-bearing uniqueness theorems or ansatzes. The document is self-contained as a descriptive call to action and requires no external verification of circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a vision paper proposing an initiative; it introduces no free parameters, unstated axioms, or invented scientific entities. The claims rest on general observations about data generation and computing constraints without additional foundational elements.

pith-pipeline@v0.9.1-grok · 5747 in / 1149 out tokens · 32929 ms · 2026-06-26T09:18:38.642432+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

299 extracted references

  1. [1]

    Bentley, Shankar Balasubramanian, Harold P

    David R. Bentley, Shankar Balasubramanian, Harold P. Swerdlow, and others. Ac- curate whole human genome sequencing using reversible terminator chemistry. InNature2008

  2. [2]

    Altman, and others

    Marcel Margulies, Michael Egholm, William E. Altman, and others. Genome sequencing in microfabricated high-density picolitre reactors. InNature2005

  3. [3]

    Porreca, Nikos B

    Jay Shendure, Gregory J. Porreca, Nikos B. Reppas, and others. Accurate Multi- plex Polony Sequencing of an Evolved Bacterial Genome. InScience2005

  4. [4]

    Harris, Phillip R

    Timothy D. Harris, Phillip R. Buzby, Hazen Babcock, and others. Single-Molecule DNA Sequencing of a Viral Genome. InScience2008

  5. [5]

    A new class of cleavable fluorescent nucleotides: synthesis and optimization as reversible terminators for DNA sequencing by synthesis†

    Gerardo Turcatti, Anthony Romieu, Milan Fedurco, and Ana-Paula Tairi. A new class of cleavable fluorescent nucleotides: synthesis and optimization as reversible terminators for DNA sequencing by synthesis†. InNucleic Acids Research2008

  6. [6]

    Stupi, Vladislav A

    Weidong Wu, Brian P. Stupi, Vladislav A. Litosh, and others. Termination of DNA synthesis by N6 -alkylated, not 3’- O -alkylated, photocleavable 2’- deoxyadenosine triphosphates. InNucleic Acids Research2007

  7. [7]

    Rapid parallel nucleic acid analysis

    Carl W Fuller. Rapid parallel nucleic acid analysis. US Patent US7264934B2. 2007

    2007

  8. [8]

    Reagents, meth- ods, and libraries for bead-based sequencing

    Kevin McKernan, Alan Blanchard, Lev Kotler, and Gina Costa. Reagents, meth- ods, and libraries for bead-based sequencing. US Patent US20090181860A1. 2008

    2008

  9. [9]

    Method for nucleic acid analysis

    Carl W Fuller and John R Nelson. Method for nucleic acid analysis. US Patent US7871771B2. 2011

    2011

  10. [10]

    Real-Time DNA Sequencing from Single Polymerase Molecules

    John Eid, Adrian Fehr, Jeremy Gray, and others. Real-Time DNA Sequencing from Single Polymerase Molecules. InScience2009

  11. [11]

    Ionic channels formed byStaphylococcus aureus alpha- toxin: Voltage-dependent inhibition by divalent and trivalent cations

    Gianfranco Menestrina. Ionic channels formed byStaphylococcus aureus alpha- toxin: Voltage-dependent inhibition by divalent and trivalent cations. InThe Journal of Membrane Biology1986

  12. [12]

    Automated forward and reverse ratcheting of DNA in a nanopore at 5-Å precision

    Gerald M Cherf, Kate R Lieberman, Hytham Rashid, and others. Automated forward and reverse ratcheting of DNA in a nanopore at 5-Å precision. In Nature Biotechnology2012

  13. [13]

    Reading DNA at single-nucleotide resolution with a mutant MspA nanopore and phi29 DNA polymerase

    Elizabeth A Manrao, Ian M Derrington, Andrew H Laszlo, and others. Reading DNA at single-nucleotide resolution with a mutant MspA nanopore and phi29 DNA polymerase. InNature Biotechnology2012

  14. [14]

    Decoding long nanopore sequencing reads of natural DNA

    Andrew H Laszlo, Ian M Derrington, Brian C Ross, and others. Decoding long nanopore sequencing reads of natural DNA. InNature Biotechnology2014

  15. [15]

    Three decades of nanopore sequencing

    David Deamer, Mark Akeson, and Daniel Branton. Three decades of nanopore sequencing. InNature Biotechnology2016

  16. [16]

    Kasianowicz, Eric Brandin, Daniel Branton, and David W

    John J. Kasianowicz, Eric Brandin, Daniel Branton, and David W. Deamer. Char- acterization of individual polynucleotide molecules using a membrane channel. InProceedings of the National Academy of Sciences1996

  17. [17]

    Rapid nanopore discrimi- nation between single polynucleotide molecules

    Amit Meller, Lucas Nivon, Eric Brandin, and others. Rapid nanopore discrimi- nation between single polynucleotide molecules. InProceedings of the National Academy of Sciences2000

  18. [18]

    Heron, Ellina Mikhailova, and others

    David Stoddart, Andrew J. Heron, Ellina Mikhailova, and others. Single- nucleotide discrimination in immobilized DNA oligonucleotides with a bio- logical nanopore. InProceedings of the National Academy of Sciences2009

  19. [19]

    Laszlo, Ian M

    Andrew H. Laszlo, Ian M. Derrington, Henry Brinkerhoff, and others. Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine with nanopore MspA. InProceedings of the National Academy of Sciences2013

  20. [20]

    Wescoe, Robin Abu-Shumays, and others

    Jacob Schreiber, Zachary L. Wescoe, Robin Abu-Shumays, and others. Error rates for nanopore discrimination among cytosine, methylcytosine, and hydrox- ymethylcytosine along individual DNA strands. InProceedings of the National Academy of Sciences2013

  21. [21]

    Butler, Mikhail Pavlenok, Ian M

    Tom Z. Butler, Mikhail Pavlenok, Ian M. Derrington, and others. Single-molecule DNA detection with an engineered MspA protein nanopore. InProceedings of the National Academy of Sciences2008

  22. [22]

    Derrington, Tom Z

    Ian M. Derrington, Tom Z. Butler, Marcus D. Collins, and others. Nanopore DNA sequencing with MspA. InProceedings of the National Academy of Sciences 2010

    2010

  23. [23]

    Hobaugh, Christopher Shustak, and others

    Langzhou Song, Michael R. Hobaugh, Christopher Shustak, and others. Structure of Staphylococcal a-Hemolysin, a Heptameric Transmembrane Pore. InScience 1996

    1996

  24. [24]

    A pore-forming protein with a metal-actuated switch

    Barbara Walker, John Kasianowicz, Musti Krishnasastry, and Hagan Bayley. A pore-forming protein with a metal-actuated switch. InProtein Engineering, Design and Selection1994

  25. [25]

    Wescoe, Jacob Schreiber, and Mark Akeson

    Zachary L. Wescoe, Jacob Schreiber, and Mark Akeson. Nanopores Discriminate among Five C5-Cytosine Variants in DNA. InJournal of the American Chemical Society2014

  26. [26]

    Lieberman, Gerald M

    Kate R. Lieberman, Gerald M. Cherf, Michael J. Doody, and others. Processive Replication of Single DNA Molecules in a Nanopore Catalyzed by phi29 DNA Polymerase. InJournal of the American Chemical Society2010

  27. [27]

    Bezrukov, Igor Vodyanoy, Rafik A

    Sergey M. Bezrukov, Igor Vodyanoy, Rafik A. Brutyan, and John J. Kasianowicz. Dynamics and Free Energy of Polymers Partitioning into a Nanoscale Pore. In Macromolecules1996

  28. [28]

    Kasianowicz, and others

    Mark Akeson, Daniel Branton, John J. Kasianowicz, and others. Microsec- ond Time-Scale Discrimination Among Polycytidylic Acid, Polyadenylic Acid, and Polyuridylic Acid as Homopolymers or as Segments Within Single RNA Molecules. InBiophysical Journal1999

  29. [29]

    Heron, Jochen Klingelhoefer, and others

    David Stoddart, Andrew J. Heron, Jochen Klingelhoefer, and others. Nucleobase Recognition in ssDNA at the Central Constriction of the a-Hemolysin Pore. In Nano Letters2010

  30. [30]

    Reza Ghadiri

    Nurit Ashkenasy, Jorge Sánchez-Quesada, Hagan Bayley, and M. Reza Ghadiri. Recognizing a Single Base in an Individual DNA Strand: A Step Toward DNA Sequencing in Nanopores. InAngewandte Chemie International Edition2005

  31. [31]

    Multiple Base- Recognition Sites in a Biological Nanopore: Two Heads are Better than One

    David Stoddart, Giovanni Maglia, Ellina Mikhailova, and others. Multiple Base- Recognition Sites in a Biological Nanopore: Two Heads are Better than One. In Angewandte Chemie International Edition2010

  32. [32]

    Bezrukov and John J

    Sergey M. Bezrukov and John J. Kasianowicz. Current noise reveals protonation kinetics and number of ionizable sites in an open protein ion channel. InPhysical Review Letters1993

  33. [33]

    A single-molecule nanopore sequencing platform

    Jia-Yuan Zhang, Yuning Zhang, Lele Wang, and others. A single-molecule nanopore sequencing platform. InbioRxiv2024

  34. [34]

    Advances and trends in omics technology develop- ment

    Xiaofeng Dai and Li Shen. Advances and trends in omics technology develop- ment. InFrontiers in Medicine2022

  35. [35]

    Smx: Heterogeneous architecture for universal sequence alignment acceleration

    Max Doblas, Po Jui Shih, Oscar Lostes-Cazorla, and others. Smx: Heterogeneous architecture for universal sequence alignment acceleration. InMICRO2025

  36. [36]

    Accelerating Genome Analysis via Algorithm- Architecture Co-Design

    Onur Mutlu and Can Firtina. Accelerating Genome Analysis via Algorithm- Architecture Co-Design. InDAC2023

  37. [37]

    From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures

    Mohammed Alser, Joel Lindegger, Can Firtina, and others. From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures. InComputational and Structural Biotechnology Journal2022

  38. [38]

    Helix: Algorithm/architecture co-design for accelerating nanopore genome base-calling

    Qian Lou, Sarath Chandra Janga, and Lei Jiang. Helix: Algorithm/architecture co-design for accelerating nanopore genome base-calling. InPACT2020

  39. [39]

    Brawl: A spintronics-based portable basecalling-in- memory architecture for nanopore genome sequencing

    Qian Lou and Lei Jiang. Brawl: A spintronics-based portable basecalling-in- memory architecture for nanopore genome sequencing. InIEEE CAL2018

  40. [40]

    Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors

    Taha Shahroodi, Gagandeep Singh, Mahdi Zahedi, and others. Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors. InMICRO2023

  41. [41]

    Benchmarking learned indexes

    Ryan Marcus, Andreas Kipf, Alexander van Renen, and others. Benchmarking learned indexes. InPVLDB2020

  42. [42]

    Accelerated seeding for genome sequence alignment with enumerated radix trees

    Arun Subramaniyan, Jack Wadden, Kush Goliya, and others. Accelerated seeding for genome sequence alignment with enumerated radix trees. InISCA2021

  43. [43]

    RADAR: A 3D-ReRAM based DNA Alignment Accelerator Architecture

    Wenqin Huangfu, Shuangchen Li, Xing Hu, and Yuan Xie. RADAR: A 3D-ReRAM based DNA Alignment Accelerator Architecture. InDAC2018

  44. [44]

    GeNVoM: Read Mapping Near Non-Volatile Memory

    S Karen Khatamifard, Zamshed Chowdhury, Nakul Pande, and others. GeNVoM: Read Mapping Near Non-Volatile Memory. InIEEE/ACM TCBB2021

  45. [45]

    RAPID: A ReRAM Processing In-memory Architecture for DNA Sequence Alignment

    Saransh Gupta, Mohsen Imani, Behnam Khaleghi, and others. RAPID: A ReRAM Processing In-memory Architecture for DNA Sequence Alignment. InISLPED 2019

    2019

  46. [46]

    PIM-Align: A Processing-in- Memory Architecture for FM-Index Search Algorithm

    Xue-Qi Li, Guang-Ming Tan, and Ning-Hui Sun. PIM-Align: A Processing-in- Memory Architecture for FM-Index Search Algorithm. InJournal of Computer Science and Technology2021

  47. [47]

    Aligns: A Processing-in- memory Accelerator for DNA Short Read Alignment Leveraging SOT-MRAM

    Shaahin Angizi, Jiao Sun, Wei Zhang, and Deliang Fan. Aligns: A Processing-in- memory Accelerator for DNA Short Read Alignment Leveraging SOT-MRAM. InDAC2019

  48. [48]

    AligneR: A Process-in- memory Architecture for Short Read Alignment in ReRAMs

    Farzaneh Zokaee, Hamid R Zarandi, and Lei Jiang. AligneR: A Process-in- memory Architecture for Short Read Alignment in ReRAMs. InIEEE CAL 2018

    2018

  49. [49]

    Darwin: A Genomics Co- processor Provides up to 15,000 x Acceleration on Long Read Assembly

    Yatish Turakhia, Gill Bejerano, and William J Dally. Darwin: A Genomics Co- processor Provides up to 15,000 x Acceleration on Long Read Assembly. In ASPLOS2018

  50. [50]

    GenAx: A Genome Sequencing Accelerator

    Daichi Fujiki, Arun Subramaniyan, Tianjun Zhang, and others. GenAx: A Genome Sequencing Accelerator. InISCA2018

  51. [51]

    Race Logic: A Hard- ware Acceleration for Dynamic Programming Algorithms

    Advait Madhavan, Timothy Sherwood, and Dmitri Strukov. Race Logic: A Hard- ware Acceleration for Dynamic Programming Algorithms. InACM SIGARCH Computer Architecture News2014

  52. [52]

    Bitmapper2: A GPU-accelerated All-mapper Based on The Sparse Q-gram Index

    Haoyu Cheng, Yong Zhang, and Yun Xu. Bitmapper2: A GPU-accelerated All-mapper Based on The Sparse Q-gram Index. InIEEE/ACM TCBB2018

  53. [53]

    Hard- ware Acceleration of BWA-MEM Genomic Short Read Mapping for Longer Read Lengths

    Ernst Joachim Houtgast, Vlad-Mihai Sima, Koen Bertels, and Zaid Al-Ars. Hard- ware Acceleration of BWA-MEM Genomic Short Read Mapping for Longer Read Lengths. InComputational Biology and Chemistry2018

  54. [54]

    An Efficient GPU-accelerated Implementation of Genomic Short Read Mapping with BWA-MEM

    Ernst Joachim Houtgast, VladMihai Sima, Koen Bertels, and Zaid AlArs. An Efficient GPU-accelerated Implementation of Genomic Short Read Mapping with BWA-MEM. InACM SIGARCH Computer Architecture News2017

  55. [55]

    Logan: High-performance GPU-based X-drop Long-read Alignment

    Alberto Zeni, Giulia Guidi, Marquita Ellis, and others. Logan: High-performance GPU-based X-drop Long-read Alignment. InIPDPS2020

  56. [56]

    GASAL2: A GPU Accelerated Sequence Alignment Library for High-Throughput NGS Data

    Nauman Ahmed, Jonathan Lévy, Shanshan Ren, and others. GASAL2: A GPU Accelerated Sequence Alignment Library for High-Throughput NGS Data. In BMC Bioinformatics2019

  57. [57]

    Accelerat- ing the Smith-waterman Algorithm Using Bitwise Parallel Bulk Computation Technique on GPU

    Takahiro Nishimura, Jacir L Bordim, Yasuaki Ito, and Koji Nakano. Accelerat- ing the Smith-waterman Algorithm Using Bitwise Parallel Bulk Computation Technique on GPU. InIPDPSW2017

  58. [58]

    CUDAlign 4.0: Incremental Speculative Traceback for Exact Chromosome- wide Alignment in GPU Clusters

    Edans Flavius de Oliveira Sandes, Guillermo Miranda, Xavier Martorell, and oth- ers. CUDAlign 4.0: Incremental Speculative Traceback for Exact Chromosome- wide Alignment in GPU Clusters. InIEEE TPDS2016

  59. [59]

    GSWABE: Faster GPU-accelerated Sequence Alignment with Optimal Alignment Retrieval for Short DNA Sequences

    Yongchao Liu and Bertil Schmidt. GSWABE: Faster GPU-accelerated Sequence Alignment with Optimal Alignment Retrieval for Short DNA Sequences. In Concurrency and Computation: Practice and Experience2015

  60. [60]

    CUDASW++ 3.0: Accel- erating Smith-Waterman Protein Database Search by Coupling CPU and GPU SIMD Instructions

    Yongchao Liu, Adrianto Wirawan, and Bertil Schmidt. CUDASW++ 3.0: Accel- erating Smith-Waterman Protein Database Search by Coupling CPU and GPU SIMD Instructions. InBMC Bioinformatics2013

  61. [61]

    CUDASW++: Optimizing Smith-Waterman Sequence Database Searches for CUDA-enabled Graphics Processing Units

    Yongchao Liu, Douglas L Maskell, and Bertil Schmidt. CUDASW++: Optimizing Smith-Waterman Sequence Database Searches for CUDA-enabled Graphics Processing Units. InBMC Research Notes2009

  62. [62]

    CUDASW++ 2.0: En- hanced Smith-Waterman Protein Database Search on CUDA-enabled GPUs Based on SIMT and Virtualized SIMD Abstractions

    Yongchao Liu, Bertil Schmidt, and Douglas L Maskell. CUDASW++ 2.0: En- hanced Smith-Waterman Protein Database Search on CUDA-enabled GPUs Based on SIMT and Virtualized SIMD Abstractions. InBMC Research Notes 2010

    2010

  63. [63]

    Arioc: High- throughput Read Alignment with GPU-accelerated Exploration of The Seed- and-extend Search Space

    Richard Wilton, Tamas Budavari, Ben Langmead, and others. Arioc: High- throughput Read Alignment with GPU-accelerated Exploration of The Seed- and-extend Search Space. InPeerJ2015

  64. [64]

    Ultra-fast Next Genera- tion Human Genome Sequencing Data Processing Using DRAGENTM Bio-IT Processor for Precision Medicine

    Amit Goyal, Hyuk Jung Kwon, Kichan Lee, and others. Ultra-fast Next Genera- tion Human Genome Sequencing Data Processing Using DRAGENTM Bio-IT Processor for Precision Medicine. InOpen Journal of Genetics2017

  65. [65]

    When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

    Yu-Ting Chen, Jason Cong, Zhenman Fang, and others. When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration. In USENIX HotCloud2016

  66. [66]

    Accelerating the Next Gener- ation Long Read Mapping with the FPGA-based System

    Peng Chen, Chao Wang, Xi Li, and Xuehai Zhou. Accelerating the Next Gener- ation Long Read Mapping with the FPGA-based System. InIEEE/ACM TCBB 2014

    2014

  67. [67]

    A High- Throughput FPGA Accelerator for Short-Read Mapping of the Whole Human Genome

    Yen-Lung Chen, Bo-Yi Chang, Chia-Hsiang Yang, and Tzi-Dar Chiueh. A High- Throughput FPGA Accelerator for Short-Read Mapping of the Whole Human Genome. InIEEE TPDS2021

  68. [68]

    SeedEx: A Genome Sequencing Accelerator for Optimal Alignments in Subminimal Space

    Daichi Fujiki, Shunhao Wu, Nathan Ozog, and others. SeedEx: A Genome Sequencing Accelerator for Optimal Alignments in Subminimal Space. InMICRO 2020

    2020

  69. [69]

    ASAP: Accelerated Short-read Alignment on Programmable Hardware

    Subho Sankar Banerjee, Mohamed El-Hadedy, Jong Bin Lim, and others. ASAP: Accelerated Short-read Alignment on Programmable Hardware. InIEEE TC 2019

    2019

  70. [70]

    FPGASW: Accelerating Large-scale Smith–Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array

    Xia Fei, Zou Dan, Lu Lina, and others. FPGASW: Accelerating Large-scale Smith–Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array. InInterdisciplinary Sciences: Computational Life Sciences 2018

    2018

  71. [71]

    Hardware- acceleration of Short-read Alignment Based on the Burrows-wheeler Transform

    Hasitha Muthumala Waidyasooriya and Masanori Hariyama. Hardware- acceleration of Short-read Alignment Based on the Burrows-wheeler Transform. InIEEE TPDS2015

  72. [72]

    A Novel High-throughput Acceleration Engine for Read Alignment

    Yu-Ting Chen, Jason Cong, Jie Lei, and Peng Wei. A Novel High-throughput Acceleration Engine for Read Alignment. InFCCM2015

  73. [73]

    SWIFOLD: Smith- Waterman Implementation on FPGA with OpenCL for Long DNA Sequences

    Enzo Rucci, Carlos Garcia, Guillermo Botella, and others. SWIFOLD: Smith- Waterman Implementation on FPGA with OpenCL for Long DNA Sequences. InBMC Systems Biology2018

  74. [74]

    An FPGA Accel- erator of the Wavefront Algorithm for Genomics Pairwise Alignment

    Abbas Haghi, Santiago Marco-Sola, Lluc Alvarez, and others. An FPGA Accel- erator of the Wavefront Algorithm for Genomics Pairwise Alignment. InFPL 2021

    2021

  75. [75]

    PipeBSW: A Two-Stage Pipeline Struc- ture for Banded Smith-Waterman Algorithm on FPGA

    Luyi Li, Jun Lin, and Zhongfeng Wang. PipeBSW: A Two-Stage Pipeline Struc- ture for Banded Smith-Waterman Algorithm on FPGA. InISVLSI2021

  76. [76]

    Genesis: A Hardware Acceleration Framework for Genomic Data Analysis

    Tae Jun Ham, David Bruns-Smith, Brendan Sweeney, and others. Genesis: A Hardware Acceleration Framework for Genomic Data Analysis. InISCA2020

  77. [77]

    Accelerating Genomic Data Analytics With Composable Hardware Acceleration Framework

    Tae Jun Ham, Yejin Lee, Seong Hoon Seo, and others. Accelerating Genomic Data Analytics With Composable Hardware Acceleration Framework. InIEEE Micro2021

  78. [78]

    Nothaft, and others

    Lisa Wu, David Bruns-Smith, Frank A. Nothaft, and others. FPGA Accelerated Indel Realignment in the Cloud. InHPCA2019

  79. [79]

    Kalsi, Zülal Bingöl, and others

    Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl, and others. GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis. InMICRO2020

  80. [80]

    Aligner-D: Leveraging In- DRAM Computing to Accelerate DNA Short Read Alignment

    Fan Zhang, Shaahin Angizi, Jiao Sun, and others. Aligner-D: Leveraging In- DRAM Computing to Accelerate DNA Short Read Alignment. InIEEE JETCAS 2023

    2023

Showing first 80 references.