Fully Digital, Standard-Cell-Based Multifunction Compute-in-Memory Arrays for Genome Sequencing - Lanius, Christian; Gemmeke, Tobias (IEEE transactions on very large scale integration (VLSI) systems, 2024)
The rapid advancement in genome sequencing technology has led to a significant increase in the number of genomic reads in recent years. Due to the immense size of reference genomes, which can be up to 3 billion bases, finding optimal solutions for through approximate string matching proves to be computationally challenging. Current alignment algorithms address this by performing a preprocessing step to efficiently calculate likely matching regions and only aligning at the base level within these regions. This article demonstrates the acceleration of sorting and searching in memories, both crucial components of genome alignment algorithms. We designed a compute-in-memory (CIM) array using standard cells, which is capable of sorting datastreams blockwise, merging sorted blocks, as well as operating as a content addressable memory (CAM) while also being able to perform multiword logic operations. We address the problem of datasets not fitting into on-chip memory by reusing the CIM array for a merge sorting step, enabling arbitrarily sized sorting. Our 2.6- μ \text{m}^{2} /bit design, fabricated using 22-nm fully depleted silicon-on-insulator (FDSOI) technology, yields a throughput of up to 4.28 GB/s at f_{\text {max}} and 4.97 nJ/sort at the minimum energy point (MEP) when executing sort operations.