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Genome sequences contain hundreds of millions of DNA base pairs. Finding thedegree of similarity between two genomes requires executing a compute-intensivedynamic programming algorithm, such as Smith-Waterman. Traditional von Neumannarchitectures have limited parallelism and cannot provide an efficient solutionfor large-scale genomic data. Approximate heuristic methods (e.g. BLAST) arecommonly used. However, they are suboptimal and still compute-intensive. Inthis work, we present BioSEAL, a Biological SEquence ALignment accelerator.BioSEAL is a massively parallel non-von Neumann processing-in-memoryarchitecture for large-scale DNA and protein sequence alignment. BioSEAL isbased on resistive content addressable memory, capable of energy-efficient andhigh-performance associative processing. We present an associative processingalgorithm for entire database sequence alignment on BioSEAL and compare itsperformance and power consumption with state-of-art solutions. We show thatBioSEAL can achieve up to 57x speedup and 156x better energy efficiency,compared with existing solutions for genome sequence alignment and proteinsequence database search.

BioSEAL: In-Memory Biological Sequence Alignment Accelerator for Large-Scale Genomic Data