Processing-in-Storage Architecture for Machine Learning and Bioinformatics

Authors: Roman Kaplan (Israel Institute of Technology), Leonid Yavits (Israel Institute of Technology), Ran Ginosar (Israel Institute of Technology)

Abstract: User-generated and bioinformatics database volumes has been increasing exponentially for more than a decade. With the slowdown and approaching end of Moore's law, traditional technologies cannot satisfy the increasing demands for processing power. This work presents PRINS, a highly-parallel in-storage processing architecture. PRINS combines non-volatile memory with processing capabilities on every bitcell. An emerging technology, memristors, form the basis for the design.

Implementations of three data-intensive and massively parallel algorithms are demonstrated: (1) Smith-Waterman DNA local sequence alignment (bioinformatics), (3) K-means clustering (machine learning) and (3) data deduplication. Performance and energy efficiency of PRINS compared to other published solutions is presented for each algorithm. PRINS is shown to achieve orders-of-magnitude improvement in performance and power efficiency over existing solutions, from large-scale bioinformatics and machine-learning to single-GPU or FPGA implementations.

Best Poster Finalist (BP): no

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