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In-Memory 3D NAND Flash Hyperdimensional Computing Engine for Energy-Efficient SARS-CoV-2 Genome Sequencing

Hyperdimensional computing (HDC) is a promising paradigm for large-scale genome sequencing. In this work, we explore the feasibility of the in-memory HDC on 3D NAND Flash for genome sequencing. We investigate the HDC engine with geographical region classification of SARS-CoV-2 genome sequences. The simulation results indicate the robustness of the classification accuracy despite the 3D NAND Flash non-idealities. The system performance is evaluated and it achieves 1.21× improvement on energy efficiency compared to PCM-based HDC engine. The area efficiency is also improved by 3.79×.