A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment
A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment
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DOI：
10.1109/esscirc59616.2023.10268783
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Fan Zhang;Wangxin He;Injune Yeo;Maximilian Liehr;Nathaniel Cady;Yu Cao;J.-s. Seo;Deliang Fan
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文献类型：
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作者：
Fan Zhang;Wangxin He;Injune Yeo;Maximilian Liehr;Nathaniel Cady;Yu Cao;J.-s. Seo;Deliang Fan
在基因组分析中，由于内存墙的挑战，主要的计算瓶颈是内存和计算密集型的DNA短读取比对。这项工作提出了第一个基于内存计算（CIM）宏设计的电阻式RAM (RRAM)，用于加速最先进的基于BWT的基因组测序比对。我们的设计可以支持对齐算法所需的所有核心指令，即基于XNOR的匹配、计数和加法。在HfO2 RRAM和65nm CMOS集成中实现的CIM宏显示出迄今为止最佳的能量效率，为2.07 TOPS/W和2。12gsuffix /J at 10 v。
In genomic analysis, the major computation bottleneck 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. 12Gsuffixes/J at 1. 0V.