{"ID":2856019,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10872","arxiv_id":"2510.10872","title":"FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.10872","url_pdf":"https://arxiv.org/pdf/2510.10872v1","authors":"[\"Sumukh Pinge\",\"Ashkan Moradifirouzabadi\",\"Keming Fan\",\"Prasanna Venkatesan Ravindran\",\"Tanvir H. Pantha\",\"Po-Kai Hsu\",\"Zheyu Li\",\"Weihong Xu\",\"Zihan Xia\",\"Flavio Ponzina\",\"Winston Chern\",\"Taeyoung Song\",\"Priyankka Ravikumar\",\"Mengkun Tian\",\"Lance Fernandes\",\"Huy Tran\",\"Hari Jayasankar\",\"Hang Chen\",\"Chinsung Park\",\"Amrit Garlapati\",\"Kijoon Kim\",\"Jongho Woo\",\"Suhwan Lim\",\"Kwangsoo Kim\",\"Wanki Kim\",\"Daewon Ha\",\"Duygu Kuzum\",\"Shimeng Yu\",\"Sourav Dutta\",\"Asif Khan\",\"Tajana Rosing\",\"Mingu Kang\"]","published":"2025-10-13T00:40:25Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}