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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 HfO2 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.
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A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment
In genomic analysis, the major computation bottle- neck is the memory- and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2.12G suffixes/J at 1.0V.
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- PAR ID:
- 10462007
- Date Published:
- Journal Name:
- Proceedings of ESSCIRC
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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