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- Title
Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.
- Authors
Li, Yijun; Tang, Jianshi; Gao, Bin; Yao, Jian; Fan, Anjunyi; Yan, Bonan; Yang, Yuchao; Xi, Yue; Li, Yuankun; Li, Jiaming; Sun, Wen; Du, Yiwei; Liu, Zhengwu; Zhang, Qingtian; Qiu, Song; Li, Qingwen; Qian, He; Wu, Huaqiang
- Abstract
In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications. Designing efficient 3D artificial neural networks chip remains a challenge. Here, the authors report a M3D-LIME chip with monolithic three-dimensional integration of hybrid memory architecture based on resistive random-access memory, which achieves a high classification accuracy of 96% in one-shot learning task while exhibiting 18.3× higher energy efficiency than GPU.
- Subjects
RANDOM access memory; ASSOCIATIVE storage; ARTIFICIAL neural networks; NETWORKS on a chip; FIELD-effect transistors; GRAPHICS processing units; FEATURE extraction
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-42981-1