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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. EXMA: A Genomics Accelerator for Exact-Matching

   https://doi.org/10.1109/HPCA51647.2021.00041  

   Jiang, Lei ; Zokaee, Farzaneh ( February 2021 , IEEE International Symposium on High-Performance Computer Architecture)

   null (Ed.)

   Genomics is the foundation of precision medicine, global food security and virus surveillance. Exact-match is one of the most essential operations widely used in almost every step of genomics such as alignment, assembly, annotation, and compression. Modern genomics adopts Ferragina-Manzini Index (FMIndex) augmenting space-efficient Burrows-Wheeler transform (BWT) with additional data structures to permit ultra-fast exact-match operations. However, FM-Index is notorious for its poor spatial locality and random memory access pattern. Prior works create GPU-, FPGA-, ASIC- and even process-in-memory (PIM)based accelerators to boost FM-Index search throughput. Though they achieve the state-of-the-art FM-Index search throughput, the same as all prior conventional accelerators, FM-Index PIMs process only one DNA symbol after each DRAM row activation, thereby suffering from poor memory bandwidth utilization. In this paper, we propose a hardware accelerator, EXMA, to enhance FM-Index search throughput. We first create a novel EXMA table with a multi-task-learning (MTL)-based index to process multiple DNA symbols with each DRAM row activation. We then build an accelerator to search over an EXMA table. We propose 2-stage scheduling to increase the cache hit rate of our accelerator. We introduce dynamic page policy to improve the row buffer hit rate of DRAM main memory. We also present CHAIN compression to reduce the data structure size of EXMA tables. Compared to state-of-the-art FM-Index PIMs, EXMA improves search throughput by 4.9 ×, and enhances search throughput per Watt by 4.8×. 

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4. Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

   https://doi.org/10.1145/3410463.3414626  

   Lou, Qian ; Janga, Sarath Chandra ; Jiang, Lei ( September 2020 , ACM International Conference on Parallel Architectures and Compilation)

   null (Ed.)

   Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes 44.5% of total execution time of a nanopore sequencing pipeline. However, it is difficult to quantize a base-caller and build a power-efficient processing-in-memory (PIM) to run the quantized base-caller. Although conventional network quantization techniques reduce the computing overhead of a base-caller by replacing floating-point multiply-accumulations by cheaper fixed-point operations, it significantly increases the number of systematic errors that cannot be corrected by read votes. The power density of prior nonvolatile memory (NVM)-based PIMs has already exceeded memory thermal tolerance even with active heat sinks, because their power efficiency is severely limited by analog-to-digital converters (ADC). Finally, Connectionist Temporal Classification (CTC) decoding and read voting cost 53.7% of total execution time in a quantized base-caller, and thus became its new bottleneck. In this paper, we propose a novel algorithm/architecture co-designed PIM, Helix, to power-efficiently and accurately accelerate nanopore base-calling. From algorithm perspective, we present systematic error aware training to minimize the number of systematic errors in a quantized base-caller. From architecture perspective, we propose a low-power SOT-MRAM-based ADC array to process analog-to-digital conversion operations and improve power efficiency of prior DNN PIMs. Moreover, we revised a traditional NVM-based dot-product engine to accelerate CTC decoding operations, and create a SOT-MRAM binary comparator array to process read voting. Compared to state-of-the-art PIMs, Helix improves base-calling throughput by 6x, throughput per Watt by 11.9x and per mm2 by 7.5x without degrading base-calling accuracy. 

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5. 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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