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  1. Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome sequencing technologies presents a significant challenge for genome analysis. Because of errors from sequencing machines and genetic variations, approximate pattern matching (APM) is a must for practical genome analysis. Recent work proposes FPGA, ASIC and even process-in-memory-based accelerators to boost the APM throughput by accelerating dynamic-programming-based algorithms (e.g., Smith-Waterman). However, existing accelerators lack the efficient hardware acceleration for the exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation and compression. State-of-the-art genome analysis adopts the FM-Index that augments the space-efficient BWT with additional data structures permitting fast EPM operations. But the FM-Index is notorious for poor spatial locality and massive random memory accesses. In this paper, we propose a ReRAM-based process-in-memory architecture, FindeR, to enhance the FM-Index EPM search throughput in genomic sequences. We build a reliable and energy-efficient Hamming distance unit to accelerate the computing kernel of FM-Index search using commodity ReRAM chips without introducing extra CMOS logic. We further architect a full-fledged FM-Index search pipeline and improve its search throughput by lightweight scheduling on the NVDIMM. We also create a system library for programmers to invoke FindeR to perform EPMs in genome analysis. Compared to state-of-the-art accelerators, FindeR improves the FM-Index search throughput by 83% ~ 30K× and throughput per Watt by 3.5×~42.5K×. 
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