ReSMA: Accelerating Approximate String Matching Using ReRAM-based Content Addressable Memory

Approximate string matching (ASM) functions as the basic operation kernel for a large number of string processing applications. Existing Von-Neumann-based ASM accelerators suffer from huge intermediate data with the ever-increasing string data, leading to massive off-chip data transmissions. This paper presents a novel ASM processing-in-memory (PIM) accelerator, namely ReSMA, based on ReCAM- and ReRAM-arrays to eliminate the off-chip data transmissions in ASM. We develop a novel ReCAM-friendly filter-and-filtering algorithm to process the q-grams filtering in ReCAM memory. We also design a new data mapping strategy and a new verification algorithm, which enables computing the edit distances totally in ReRAM crossbars for energy saving. Experimental results show that ReSMA outperforms the CPU-, GPU-, FPGA-, ASIC-, and PIM-based solutions by 268.7x, 38.6x, 20.9x, 707.8x, and 14.7x in terms of performance, and 153.8x, 42.2x, 31.6x, 18.3x, and 5.3x in terms of energy-saving, respectively.