An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage
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arXiv
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| Natura: | Preprint |
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2025
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| author | Zhao, Zijian Parekh, Varun Darshana Hsu, Po-Kai Qin, Yixin Song, Yiming Islam, A N M Nafiul Cao, Ningyuan Joshi, Siddharth Kämpfe, Thomas Jung, Moonyoung Seo, Kwangyou Kim, Kwangsoo Kim, Wanki Ha, Daewon Dutta, Sourav Sengupta, Abhronil Gong, Xiao Yu, Shimeng Narayanan, Vijaykrishnan Ni, Kai |
| author_facet | Zhao, Zijian Parekh, Varun Darshana Hsu, Po-Kai Qin, Yixin Song, Yiming Islam, A N M Nafiul Cao, Ningyuan Joshi, Siddharth Kämpfe, Thomas Jung, Moonyoung Seo, Kwangyou Kim, Kwangsoo Kim, Wanki Ha, Daewon Dutta, Sourav Sengupta, Abhronil Gong, Xiao Yu, Shimeng Narayanan, Vijaykrishnan Ni, Kai |
| contents | Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based multi-level cell (MLC) memory elements, such as ferroelectric field-effect transistors (FeFETs), and mapping a pixel's temporal sequence onto consecutive word lines (WLs), we enable direct temporal pattern detection within NAND strings. Each NAND string serves as a dedicated reference for a single pixel, while different blocks store patterns for distinct pixels, allowing large-scale spatial-temporal pattern recognition via simple direct bit-line (BL) sensing, a well-established operation in vertical NAND storage. We experimentally validate our approach at both the cell and array levels, demonstrating that vertical NAND-based detector achieves more than six orders of magnitude improvement in energy efficiency and more than three orders of magnitude reduction in latency compared to conventional CPU-based methods. These findings establish vertical NAND storage as a scalable and energy-efficient solution for next-generation neuromorphic vision processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage Zhao, Zijian Parekh, Varun Darshana Hsu, Po-Kai Qin, Yixin Song, Yiming Islam, A N M Nafiul Cao, Ningyuan Joshi, Siddharth Kämpfe, Thomas Jung, Moonyoung Seo, Kwangyou Kim, Kwangsoo Kim, Wanki Ha, Daewon Dutta, Sourav Sengupta, Abhronil Gong, Xiao Yu, Shimeng Narayanan, Vijaykrishnan Ni, Kai Emerging Technologies Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based multi-level cell (MLC) memory elements, such as ferroelectric field-effect transistors (FeFETs), and mapping a pixel's temporal sequence onto consecutive word lines (WLs), we enable direct temporal pattern detection within NAND strings. Each NAND string serves as a dedicated reference for a single pixel, while different blocks store patterns for distinct pixels, allowing large-scale spatial-temporal pattern recognition via simple direct bit-line (BL) sensing, a well-established operation in vertical NAND storage. We experimentally validate our approach at both the cell and array levels, demonstrating that vertical NAND-based detector achieves more than six orders of magnitude improvement in energy efficiency and more than three orders of magnitude reduction in latency compared to conventional CPU-based methods. These findings establish vertical NAND storage as a scalable and energy-efficient solution for next-generation neuromorphic vision processing. |
| title | An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2503.23685 |