Genome sequences contain hundreds of millions of DNA base pairs. Finding the degree of similarity between two genomes requires executing a compute-intensive dynamic programming algorithm, such as Smith-Waterman. Traditional von Neumann architectures have limited parallelism and cannot provide an efficient solution for large-scale genomic data. Approximate heuristic methods (e.g. BLAST) are commonly used. However, they are suboptimal and still compute-intensive. In this work, we present BioSEAL, a biological sequence alignment accelerator. BioSEAL is a massively parallel non-von Neumann processing-in-memory architecture for large-scale DNA and protein sequence alignment. BioSEAL is based on resistive content addressable memory, capable of energy-efficient and highperformance associative processing. We present an associative processing algorithm for entire database sequence alignment on BioSEAL and compare its performance and power consumption with state-of-art solutions. We show that BioSEAL can achieve up to 57× speedup and 156× better energy efficiency, compared with existing solutions for genome sequence alignment and protein sequence database search.
|Title of host publication||SYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference|
|Publisher||Association for Computing Machinery|
|Number of pages||13|
|State||Published - 30 May 2020|
|Event||13th ACM International Systems and Storage Conference, SYSTOR 2020 - Haifa, Israel|
Duration: 13 Oct 2020 → 15 Oct 2020
|Name||SYSTOR 2020 - Proceedings of the 13th ACM International Systems and Storage Conference|
|Conference||13th ACM International Systems and Storage Conference, SYSTOR 2020|
|Period||13/10/20 → 15/10/20|
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