Genome sequence analysis is a powerful tool in medical and scientific research. Considering the inevitable sequencing errors and genetic variations, approximate string matching (ASM) has been adopted in practice for genome sequencing. However, with exponentially increasing bio-data, ASM hardware acceleration is facing severe challenges in improving the throughput and energy efficiency with the accuracy constraint. This paper presents ASMCap, an ASM acceleration approach for genome sequence analysis with hardware-algorithm co-optimization. At the circuit level, ASMCap adopts charge-domain computing based on the capacitive multi-level content addressable memories (ML-CAMs), and outperforms the state-of-the-art ML-CAM-based ASM accelerators EDAM with higher accuracy and energy efficiency. ASMCap also has misjudgment correction capability with two proposed hardware-friendly strategies, namely the Hamming-Distance Aid Correction (HDAC) for the substitution-dominant edits and the Threshold-Aware Sequence Rotation (TASR) for the consecutive indels. Evaluation results show that ASMCap can achieve an average of 1.2x (from 74.7% to 87.6%) and up to 1.8x (from 46.3% to 81.2%) higher F1 score (the key metric of accuracy), 1.4x speedup, and 10.8x energy efficiency improvement compared with EDAM. Compared with the other ASM accelerators, including ResMA based on the comparison matrix, and SaVI based on the seeding strategy, ASMCap achieves an average improvement of 174x and 61x speedup, and 8.7e3x and 943x higher energy efficiency, respectively.

pdf/asmcap-an-approximate-string-matching-accelerator-for-genome-1lzei7qr.pdf

ASMCap: An Approximate String Matching Accelerator for Genome Sequence Analysis Based on Capacitive Content Addressable Memory

Graph convolutional network (GCN) has emerged as a powerful model for many graph-related tasks. In conventional von Neumann architectures, massive data movement and irregular memory access in GCN computation severely degrade the performance and computation efficiency. For GCN acceleration, processing-in-memory (PIM) is promising by reducing the data movement. However, with the emergence of large GCN computation tasks, existing 2D PIM GCN accelerators face the challenge of storing all the necessary data on chip due to the limited PIM memory capacity, resulting in unwanted external memory access and degradation of performance and energy efficiency. This paper presents Fe-GCN, a 3D PIM GCN accelerator with high memory density based on the ferroelectric field-effect transistor (FeFET) memory. Besides, to mitigate the impact of the increased latency of the 3D memory structure, several software-hardware co-optimizations are proposed. Furthermore, an edge merging technique is also proposed to increase the memory utilization for the 3D GCN mapping and computing. Experimental results show that Fe-GCN achieves on average 2,647x, 58x, 18x, and 35x speedup and 26,708x, 1,246x, 25x, and 57x energy efficiency improvement over CPU, GPU, the state-of-the-art accelerators based on RRAM PIM and ASIC, respectively.

Fe-GCN: A 3D FeFET Memory Based PIM Accelerator for Graph Convolutional Networks

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ASMCap: An Approximate String Matching Accelerator for Genome Sequence Analysis Based on Capacitive Content Addressable Memory

Fe-GCN: A 3D FeFET Memory Based PIM Accelerator for Graph Convolutional Networks