Counteractive Coupling IGZO/CNT Hybrid 2T0C DRAM Accelerating RRAM-based Computing-In-Memory via Monolithic 3D Integration for Edge AI
Mingcheng Shi(Tsinghua University), Huaqiang Wu(Beijing Advanced Sciences and Innovation Center), Yanbo Su(Tsinghua University), Qingwen Li(University of Science and Technology Beijing), Yuan He(Shandong Normal University), Yuankun Li(Tsinghua University), Yue Xi(Tsinghua University), Yiwei Du(Sichuan University), Jianshi Tang(Tsinghua University), Song Qiu(Murdoch University), Ran An(Case Western Reserve University), Jiaming Li(Tsinghua University), Yijun Li(Institute of Microelectronics), He Qian(University of Science and Technology of China), Jian Yao(Suzhou Institute of Nano-tech and Nano-bionics), Liyang Pan(Institute of Microelectronics), Ruofei Hu(Hubei University), Qingtian Zhang(Tsinghua University), Bin Gao(Chinese Academy of Sciences)
Cited by 21
Related Papers
Fully hardware-implemented memristor convolutional neural network
|Nature|2020|2.2k
Switching of perpendicular magnetization by spin–orbit torques in the absence of external magnetic fields
|Nature Nanotechnology|2014|1k
Face classification using electronic synapses
|Nature Communications|2017|909
Neuro-inspired computing chips
|Nature Electronics|2020|886
A compute-in-memory chip based on resistive random-access memory
|Nature|2022|882