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Classified as a complex big data analytics problem, DNA short read alignment serves as a major sequential bottleneck to massive amounts of data generated by next-generation sequencing platforms. With Von-Neumann computing architectures struggling to address such computationally-expensive and memory-intensive task today, Processing-in-Memory (PIM) platforms are gaining growing interests. In this paper, an energy-efficient and parallel PIM accelerator (AlignS) is proposed to execute DNA short read alignment based on an optimized and hardware-friendly alignment algorithm. We first develop AlignS platform that harnesses SOT-MRAM as computational memory and transforms it to a fundamental processing unit for short read alignment. Accordingly, we present a novel, customized, highly parallel read alignment algorithm that only seeks the proposed simple and parallel in-memory operations (i.e. comparisons and additions). AlignS is then optimized through a new correlated data partitioning and mapping methodology that allows local storage and processing of DNA sequence to fully exploit the algorithm-level's parallelism, and to accelerate both exact and inexact matches. The device-to-architecture co-simulation results show that AlignS improves the short read alignment throughput per Watt per mm^2 by ~12X compared to the ASIC accelerator. Compared to recent FM-index-based ReRAM platform, AlignS achieves 1.6X higher throughput per Watt.
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PARC: A Processing-in-CAM Architecture for Genomic Long Read Pairwise Alignment using ReRAM
Technological advances in long read sequences have greatly facilitated the development of genomics. However, managing and analyzing the raw genomic data that outpaces Moore's Law requires extremely high computational efficiency. On the one hand, existing software solutions can take hundreds of CPU hours to complete human genome alignment. On the other hand, the recently proposed hardware platforms achieve low processing throughput with significant overhead. In this paper, we propose PARC, an Processing-in-Memory architecture for long read pairwise alignment leveraging emerging resistive CAM (content-addressable memory) to accelerate the bottleneck chaining step in DNA alignment. Chaining takes 2-tuple anchors as inputs and identifies a set of correlated anchors as potential alignment candidates. Unlike traditional main memory which organizes relational data structure in a linear address space, PARC stores tuples in two neighboring crossbar arrays with shared row decoder such that column-wise in-memory computational operations and row-wise memory accesses can be performed in-situ in a symmetric crossbar structure. Compared to both software tools and state-of-the-art accelerators, PARC shows significant improvement in alignment throughput and energy efficiency, thanks to the in-site computation capability and optimized data mapping.
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- PAR ID:
- 10179360
- Date Published:
- Journal Name:
- 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)
- Page Range / eLocation ID:
- 175 to 180
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASP-DAC47756.2020.9045555
