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DNA Pre-alignment Filter using Processing Near Racetrack Memory

Reference

Fazal Hameed, Asif Ali Khan, Sebastien Ollivier, Alex K. Jones, Jeronimo Castrillon, "DNA Pre-alignment Filter using Processing Near Racetrack Memory", In IEEE Computer Architecture Letters, IEEE, pp. 1–4, Jul 2022. [doi]

Abstract

Recent DNA pre-alignment filter designs employ DRAM for storing the reference genome and its associated meta-data. However, DRAM incurs increasingly high energy consumption background and refresh energy as devices scale. To overcome this problem, this paper explores a design with racetrack memory (RTM)–an emerging non-volatile memory that promises higher storage density, faster access latency, and lower energy consumption. Multi-bit storage cells in RTM are inherently sequential and thus require data placement strategies to mitigate the performance and energy impacts of shifting during data accesses. We propose a near-memory pre-alignment filter with a novel data mapping and several shift reduction strategies designed explicitly for RTM. On a set of four input genomes from the 1000 Genome Project, our approach improves performance and energy efficiency by 68% and 52%, respectively, compared to the state of the art proposed DRAM-based architecture.

Bibtex

@Article{hameed_ieeecal22,
author = {Fazal Hameed and Asif Ali Khan and Sebastien Ollivier and Alex K. Jones and Jeronimo Castrillon},
date = {2022-08},
journal = {IEEE Computer Architecture Letters},
title = {DNA Pre-alignment Filter using Processing Near Racetrack Memory},
abstract = {Recent DNA pre-alignment filter designs employ DRAM for storing the reference genome and its associated meta-data. However, DRAM incurs increasingly high energy consumption background and refresh energy as devices scale. To overcome this problem, this paper explores a design with racetrack memory (RTM)--an emerging non-volatile memory that promises higher storage density, faster access latency, and lower energy consumption. Multi-bit storage cells in RTM are inherently sequential and thus require data placement strategies to mitigate the performance and energy impacts of shifting during data accesses. We propose a near-memory pre-alignment filter with a novel data mapping and several shift reduction strategies designed explicitly for RTM. On a set of four input genomes from the 1000 Genome Project, our approach improves performance and energy efficiency by 68\% and 52\%, respectively, compared to the state of the art proposed DRAM-based architecture.},
month = jul,
numpages = {4},
publisher = {IEEE},
year = {2022},
doi = {10.1109/LCA.2022.3194263},
pages = {1--4},
url = {https://ieeexplore.ieee.org/document/9841612},
}

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