SpinLiM: Spin Orbit Torque Memory for Ternary Neural Networks Based on the Logic-in-Memory Architecture

Author:

Luo Lichuan,Zhang He,Bai Jinyu,Zhang Youguang,Kang Wang,Zhao Weisheng

Funder

Beijing Nova Program from Beijing Municipal Science and Technology Commission

Beijing Natural Science Foundation

National Natural Science Foundation of China

Publisher

IEEE

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. iMAT: Energy-Efficient In-Memory Acceleration for Ternary Neural Networks With Sparse Dot Product;2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED);2023-08-07

2. FAT: An In-Memory Accelerator With Fast Addition for Ternary Weight Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-03

3. Hadamard product-based in-memory computing design for floating point neural network training;Neuromorphic Computing and Engineering;2023-02-24

4. NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration;Science China Information Sciences;2023-02-09

5. In-Memory Computing Paradigms by Exploiting the Intrinsic Interactions Between STT and SOT;IEEE Transactions on Electron Devices;2022-12

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