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

Zokaee, Farzaneh; Zarandi, Hamid R.; Jiang, Lei

doi:10.1109/lca.2018.2854700

Cited by 29 publications

(23 citation statements)

References 16 publications

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“…Edit distance is the primary calculation metric used to quantitatively measure dissimilarity between two sequences ( Fei et al , 2018 ). Thus, it is fundamental within SRA and typically implemented via the Levenshtein or Hamming distance calculation ( Zokaee et al., 2018 ). Hamming distance is defined between two sequences of equal length, where the returned value is the number of positions with a mismatch ( Doan et al , 2012 ).…”

Section: Genome Sequencing and Genomic Data Analysismentioning

confidence: 99%

“…In addition, the use of MapReduce frameworks in hardware and cluster implementations (software) with lossless compression methodologies, have attempted to bring the unfathomable data quantity to more manageable proportions ( Al-Absi and Kang, 2015 ; Jourdren et al , 2012 ). Table 2 provides a comparison between the four key hardware acceleration approaches representing the current state-of-the-art; AligneR ( Zokaee et al , 2018 ), ASAP ( Banerjee et al , 2019 ), FPGASW ( Fei et al , 2018 ) and Darwin ( Turakhia et al , 2019 ). The technologies chosen have significantly improved the speed of sequence alignment and serve to illustrate the computational challenges and opportunities discussed in this review.…”

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

“…Levenshtein distance calculations are typically performed sequentially on CPU by most alignment algorithms limiting data throughput. Notably, AligneR and ASAP have shown a considerable acceleration in their computation through the implementation of different dedicated hardware ( Banerjee et al , 2019 ; Zokaee et al , 2018 ). AligneR uses specialized ReRAM devices instead of logic blocks where ReRAM modules are set to logic one and reset to logic zero, corresponding to different alignment scores as per the Hamming distance calculation ( Zokaee et al , 2018 ).…”

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

“…Notably, AligneR and ASAP have shown a considerable acceleration in their computation through the implementation of different dedicated hardware ( Banerjee et al , 2019 ; Zokaee et al , 2018 ). AligneR uses specialized ReRAM devices instead of logic blocks where ReRAM modules are set to logic one and reset to logic zero, corresponding to different alignment scores as per the Hamming distance calculation ( Zokaee et al , 2018 ). ASAP implements Levenshtein distance calculations in sequential logic using FPGAs in which parameters are coded into clock cycle delays and operators to logic gates ( Banerjee et al , 2019 ).…”

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

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Hardware acceleration of genomics data analysis: challenges and opportunities

2021

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The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

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Section: Genome Sequencing and Genomic Data Analysismentioning

confidence: 99%

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

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Hardware acceleration of genomics data analysis: challenges and opportunities

2021

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“…Other exotic solutions [19], [20] target Smith-Waterman algorithm. Another PIM acceleration for BWA, significantly slower than BWA-CRAM, uses ReRAM [22]. Finally, [4] covers a basic CRAM design for large-scale string matching.…”

Section: Related Workmentioning

confidence: 99%

A DNA Read Alignment Accelerator Based on Computational RAM

Chowdhury

Zabihi

Zhao

et al. 2020

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Recent years have witnessed an increasing interest in the processing-in-memory (PIM) paradigm in computing due to its promise to improve the performance through the reduction of energyhungry and long-latency memory accesses. Joined with the explosion of data to be processed, produced in genomics-particularly genome sequencing-PIM has become a potential promising candidate for accelerating genomics applications since they do not scale up well in conventional von Neumann systems. In this article, we present an in-memory accelerator architecture for DNA read alignment. This architecture outperforms corresponding software implementation by >49X and >18 000X, in terms of throughput and energy efficiency, respectively, even under conservative assumptions. INDEX TERMS Accelerator, BWA, computational RAM (CRAM), DNA, genome sequence, processing in memory (PIM), spin Hall effect magnetic tunnel junction (SHE-MTJ).

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