Multi-bit Ferroelectric-NAND for High-throughput Massive Database Search

Prasanna Venkatesan; Tanvir H. Pantha; Po-Kai Hsu; Sumukh Pinge; Zheyu Li; Chinsung Park; Priyankka Ravikumar; Hari Jayasankar; Lance Fernandes; Weihong Xu; Zihan Xia; Flavio Ponzina; Keming Fan; Amrit Garlapati; Huy Tran; Taeyoung Song; Mengkun Tian; Hang Chen; Winston Chern; Kijoon Kim; Kwangyou Seo; Suhwan Lim; Kwangsoo Kim; Wanki Kim; Daewon Ha; Duygu Kuzum; Shimeng Yu; Mingu Kang; Tajana Rosing; Sourav Dutta; Asif Khan

arXiv · 2610.05002

Multi-bit Ferroelectric-NAND for High-throughput Massive Database Search

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Abstract

The growing demand for large-scale database search in data-intensive applications, ranging from proteomics to autonomous systems, has exposed fundamental limitations in von Neumann architectures due to memory bandwidth and energy constraints. Hyperdimensional (HD) computing offers a robust and parallelizable framework for such tasks, but its practical implementation remains challenged by high memory demands. Ultra-high-density, energy-efficient ferroelectric NAND (FE-NAND) memory provides a potential solution by enabling in-situ computation. We fabricate quad-level FE-NAND strings with wide memory windows, disturb resilience, and robust retention. Using these planar FE-NAND strings as building blocks, we experimentally demonstrate in-situ multi-level cell (MLC) dot product operations at the single-cell level and, using experimentally calibrated physics-based simulations, demonstrate Hamming similarity calculations between reference and query hypervectors (HV). This platform leverages the inherent error tolerance of HD computing to achieve >90% search accuracy even at high logic levels (TLC, QLC). When benchmarked on Open Modification Search (OMS) tasks in proteomics with TB-scale datasets, our system shows nearly 1,000x speedup and over 10,000x energy efficiency improvement compared to incumbent solutions. These results establish FE-NAND as a viable in-storage compute architecture for large-scale, high-dimensional data processing.

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Prasanna Venkatesan, Tanvir H. Pantha, Po-Kai Hsu, Sumukh Pinge, Zheyu Li, Chinsung Park, Priyankka Ravikumar, Hari Jayasankar, Lance Fernandes, Weihong Xu, Zihan Xia, Flavio Ponzina, Keming Fan, Amrit Garlapati, Huy Tran, Taeyoung Song, Mengkun Tian, Hang Chen, Winston Chern, Kijoon Kim, Kwangyou Seo, Suhwan Lim, Kwangsoo Kim, Wanki Kim, Daewon Ha, Duygu Kuzum, Shimeng Yu, Mingu Kang, Tajana Rosing, Sourav Dutta, Asif Khan. 2026-10-04. Multi-bit Ferroelectric-NAND for High-throughput Massive Database Search. https://arxiv.org/abs/2610.05002

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