FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash

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Bibliographic Details
Main Authors:	Pinge, Sumukh, Moradifirouzabadi, Ashkan, Fan, Keming, Ravindran, Prasanna Venkatesan, Pantha, Tanvir H., Hsu, Po-Kai, Li, Zheyu, Xu, Weihong, Xia, Zihan, Ponzina, Flavio, Chern, Winston, Song, Taeyoung, Ravikumar, Priyankka, Tian, Mengkun, Fernandes, Lance, Tran, Huy, Jayasankar, Hari, Chen, Hang, Park, Chinsung, Garlapati, Amrit, Kim, Kijoon, Woo, Jongho, Lim, Suhwan, Kim, Kwangsoo, Kim, Wanki, Ha, Daewon, Kuzum, Duygu, Yu, Shimeng, Dutta, Sourav, Khan, Asif, Rosing, Tajana, Kang, Mingu
Format:	Preprint
Published:	2025
Subjects:	Hardware Architecture
Online Access:	Acceder al recurso
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_version_	1866918159270805504
author	Pinge, Sumukh Moradifirouzabadi, Ashkan Fan, Keming Ravindran, Prasanna Venkatesan Pantha, Tanvir H. Hsu, Po-Kai Li, Zheyu Xu, Weihong Xia, Zihan Ponzina, Flavio Chern, Winston Song, Taeyoung Ravikumar, Priyankka Tian, Mengkun Fernandes, Lance Tran, Huy Jayasankar, Hari Chen, Hang Park, Chinsung Garlapati, Amrit Kim, Kijoon Woo, Jongho Lim, Suhwan Kim, Kwangsoo Kim, Wanki Ha, Daewon Kuzum, Duygu Yu, Shimeng Dutta, Sourav Khan, Asif Rosing, Tajana Kang, Mingu
author_facet	Pinge, Sumukh Moradifirouzabadi, Ashkan Fan, Keming Ravindran, Prasanna Venkatesan Pantha, Tanvir H. Hsu, Po-Kai Li, Zheyu Xu, Weihong Xia, Zihan Ponzina, Flavio Chern, Winston Song, Taeyoung Ravikumar, Priyankka Tian, Mengkun Fernandes, Lance Tran, Huy Jayasankar, Hari Chen, Hang Park, Chinsung Garlapati, Amrit Kim, Kijoon Woo, Jongho Lim, Suhwan Kim, Kwangsoo Kim, Wanki Ha, Daewon Kuzum, Duygu Yu, Shimeng Dutta, Sourav Khan, Asif Rosing, Tajana Kang, Mingu
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2510_10872
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash Pinge, Sumukh Moradifirouzabadi, Ashkan Fan, Keming Ravindran, Prasanna Venkatesan Pantha, Tanvir H. Hsu, Po-Kai Li, Zheyu Xu, Weihong Xia, Zihan Ponzina, Flavio Chern, Winston Song, Taeyoung Ravikumar, Priyankka Tian, Mengkun Fernandes, Lance Tran, Huy Jayasankar, Hari Chen, Hang Park, Chinsung Garlapati, Amrit Kim, Kijoon Woo, Jongho Lim, Suhwan Kim, Kwangsoo Kim, Wanki Ha, Daewon Kuzum, Duygu Yu, Shimeng Dutta, Sourav Khan, Asif Rosing, Tajana Kang, Mingu Hardware Architecture 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.
title	FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash
topic	Hardware Architecture
url	https://arxiv.org/abs/2510.10872