A 65-nm RRAM Compute-in-Memory Macro for Genome Processing
Fan Zhang, Amitesh Sridharan, Wangxin He, Injune Yeo, Maximilian Liehr, Wei Zhang, Nathaniel C. Cady, Yu Cao, Jae-sun Seo, Deliang Fan
Abstract
This work presents the first resistive random access memory (RRAM)-based compute-in-memory (CIM) macro design tailored for genome processing. We analyze and demonstrate two key types of genome processing applications using our developed CIM chip prototype: the state-of-the-art (SOTA) burrows–wheeler transform (BWT)-based DNA short-read alignment and alignment-free mRNA quantification. Our CIM macro is designed and optimized to support the major functions essential to these algorithms, e.g., parallel xnor operations, count, addition, and parallel bit-wise and operations. The proposed CIM macro prototype is fabricated with monolithic integration of HfO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_2$</tex-math> </inline-formula> RRAM and 65-nm CMOS, achieving 2.07 TOPS/W (tera-operations per second per watt) and 2.12 G suffixes/J (suffixes per joule) at 1.0 V, which is the most energy-efficient solution to date for genome processing.