The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical component for drug discovery. Traditional processors struggle to handle this data volume efficiently, making in-storage computing (ISP) a promising alternative. This work introduces an ISP architecture leveraging a 3D Ferroelectric NAND (FeNAND) structure, providing significantly higher density, faster speeds, and lower voltage requirements compared to traditional NAND flash. Despite its superior density, the NAND structure has not been widely utilized in ISP applications due to limited throughput associated with row-by-row reads from serially connected cells. To overcome these limitations, we integrate hyperdimensional computing (HDC), a brain-inspired paradigm that enables highly parallel processing with simple operations and strong error tolerance. By combining HDC with the proposed dual-bound approximate matching (D-BAM) distance metric, tailored to the FeNAND structure, we parallelize vector computations to enable efficient MS spectral library search, achieving 43x speedup and 21x higher energy efficiency over state-of-the-art 3D NAND methods, while maintaining comparable accuracy.
@article{arxiv.2510.10872,
title = {FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash},
author = {Sumukh Pinge and Ashkan Moradifirouzabadi and Keming Fan and Prasanna Venkatesan Ravindran and Tanvir H. Pantha and Po-Kai Hsu and Zheyu Li and Weihong Xu and Zihan Xia and Flavio Ponzina and Winston Chern and Taeyoung Song and Priyankka Ravikumar and Mengkun Tian and Lance Fernandes and Huy Tran and Hari Jayasankar and Hang Chen and Chinsung Park and Amrit Garlapati and Kijoon Kim and Jongho Woo and Suhwan Lim and Kwangsoo Kim and Wanki Kim and Daewon Ha and Duygu Kuzum and Shimeng Yu and Sourav Dutta and Asif Khan and Tajana Rosing and Mingu Kang},
journal= {arXiv preprint arXiv:2510.10872},
year = {2025}
}