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1. Exploring DNA Alignment-in-Memory Leveraging Emerging SOT-MRAM

   https://doi.org/10.1145/3386263.3407590  

   Angizi, Shaahin ; Zhang, Wei ; Fan, Deliang ( September 2020 , Proceedings of the 2020 on Great Lakes Symposium on VLSI)

   null (Ed.)

   In this work, we review two alternative Processing-in-Memory (PIM) accelerators based on Spin-Orbit-Torque Magnetic Random Access Memory (SOT-MRAM) to execute DNA short read alignment based on an optimized and hardware-friendly alignment algorithm. We first discuss the reconstruction of the existing sequence alignment algorithm based on BWT and FM-index such that it can be fully implemented leveraging PIM functions. We then transform SOT-MRAM array to a potential computational memory by presenting two different reconfigurable sense amplifiers to accelerate the reconstructed alignment-in-memory algorithm. The cross-layer simulation results show that such PIM platforms are able to achieve a nearly ten-fold and two-fold increases in throughput/power/area measure compared with recent ASIC and processing-in-ReRAM designs, respectively. 

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2. PIM-Assembler: A Processing-in-Memory Platform for Genome Assembly

   https://doi.org/10.1109/DAC18072.2020.9218653  

   Angizi, Shaahin ; Fahmi, Naima Ahmed ; Zhang, Wei ; Fan, Deliang ( July 2020 , 57th Annual Design Automation Conference 2020)

   null (Ed.)

   In this paper, for the first time, we propose a high-throughput and energy-efficient Processing-in-DRAM-accelerated genome assembler called PIM-Assembler based on an optimized and hardware-friendly genome assembly algorithm. PIM-Assembler can assemble large-scale DNA sequence dataset from all-pair overlaps. We first develop PIM-Assembler platform that harnesses DRAM as computational memory and transforms it to a fundamental processing unit for genome assembly. PIM-Assembler can perform efficient X(N)OR-based operations inside DRAM incurring low cost on top of commodity DRAM designs (~5% of chip area). PIM-Assembler is then optimized through a correlated data partitioning and mapping methodology that allows local storage and processing of DNA short reads to fully exploit the genome assembly algorithm-level's parallelism. The simulation results show that PIM-Assembler achieves on average 8.4× and 2.3 wise× higher throughput for performing bulk bit-XNOR-based comparison operations compared with CPU and recent processing-in-DRAM platforms, respectively. As for comparison/addition-extensive genome assembly application, it reduces the execution time and power by ~5× and ~ 7.5× compared to GPU. 

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3. A 65-nm RRAM Compute-in-Memory Macro for Genome Processing

   https://doi.org/10.1109/JSSC.2024.3396429  

   Zhang, Fan ; Sridharan, Amitesh ; He, Wangxin ; Yeo, Injune ; Liehr, Maximilian ; Zhang, Wei ; Cady, Nathaniel ; Cao, Yu ; Seo, Jae-Sun ; Fan, Deliang ( July 2024 , IEEE Journal of Solid-State Circuits)

   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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4. PIM-Quantifier: A Processing-in-Memory Platform for mRNA Quantification

   https://doi.org/10.1109/DAC18074.2021.9586144  

   Zhang, Fan ; Angizi, Shaahin ; Fahmi, Naima Ahmed ; Zhang, Wei ; Fan, Deliang ( December 2021 , 2021 58th ACM/IEEE Design Automation Conference (DAC))

   Processing-in-memory (PIM) architecture has been considered as a promising solution for the “memory-wall” issue in many data-intensive applications, especially in bioinformatics. Recent works of developing PIM for genome alignment and assembling have achieved tremendous improvement, while another important genome analysis - mRNA quantification has not been explored. Efficient and accurate mRNA quantification is a crucial step for molecular signature identification, disease outcome prediction and drug development. In this paper, for the first time, we propose a SOT-MRAM based PIM platform, named PIM-Quantifier, for efficient mRNA quantification. A PIM-friendly alignment-free quantification algorithm is first proposed. Then, we present the optimized PIM architecture/circuit designs and mapping method to efficiently accelerate mRNA quantification. Extensive experiments show that PIM-Quantifier significantly improves mRNA quantification performance than CPU and recent other PIM platforms in efficiency defined as throughput/power. 

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5. PARC: A Processing-in-CAM Architecture for Genomic Long Read Pairwise Alignment using ReRAM

   https://doi.org/10.1109/ASP-DAC47756.2020.9045555  

   Chen, Fan ; Song, Linghao ; Li, Hai ; Chen, Yiran ( January 2020 , 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC))

   Technological advances in long read sequences have greatly facilitated the development of genomics. However, managing and analyzing the raw genomic data that outpaces Moore's Law requires extremely high computational efficiency. On the one hand, existing software solutions can take hundreds of CPU hours to complete human genome alignment. On the other hand, the recently proposed hardware platforms achieve low processing throughput with significant overhead. In this paper, we propose PARC, an Processing-in-Memory architecture for long read pairwise alignment leveraging emerging resistive CAM (content-addressable memory) to accelerate the bottleneck chaining step in DNA alignment. Chaining takes 2-tuple anchors as inputs and identifies a set of correlated anchors as potential alignment candidates. Unlike traditional main memory which organizes relational data structure in a linear address space, PARC stores tuples in two neighboring crossbar arrays with shared row decoder such that column-wise in-memory computational operations and row-wise memory accesses can be performed in-situ in a symmetric crossbar structure. Compared to both software tools and state-of-the-art accelerators, PARC shows significant improvement in alignment throughput and energy efficiency, thanks to the in-site computation capability and optimized data mapping. 

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