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STT-BSNN: An In-Memory Deep Binary Spiking Neural ...
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由 VT Nguyen 著作2021被引用 20 次 — This paper proposes an in-memory binary spiking neural network (BSNN) based on spin-transfer-torque magnetoresistive RAM (STT-MRAM).
STT-BSNN: An In-Memory Deep Binary Spiking Neural ...
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2021年11月8日 — This paper proposes an in-memory binary spiking neural network (BSNN) based on spin-transfer-torque magnetoresistive RAM (STT-MRAM).
STT-BSNN-An-In-Memory-Deep-Binary-Spiking-Neural- ...
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由 VT Nguyen 著作2021被引用 20 次 — ABSTRACT This paper proposes an in-memory binary spiking neural network (BSNN) based on spin- transfer-torque magnetoresistive RAM (STT-MRAM).
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STT-BSNN: An In-Memory Deep Binary Spiking Neural ...
Le Quy Don Technical University
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Le Quy Don Technical University
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由 VT Nguyen 著作2021被引用 20 次 — STT-BSNN: An In-Memory Deep Binary Spiking Neural Network Based on STT-MRAM ; Article · Faculties > Faculty of Radio-Electronic Engineering · 10.1109/ACCESS.
Quang-Kien Trinh
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STT-BSNN: An in-memory deep binary spiking neural network based on STT-MRAM. VT Nguyen, QK Trinh, R Zhang, Y Nakashima. IEEE Access 9, 151373-151385, 2021. 18 ...
NUTS-BSNN: A non-uniform time-step binarized spiking ...
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由 VN Dinh 著作2023被引用 6 次 — NUTS-BSNN is a fully binarized spiking neural network with all binary weights, including the input and output layers.
Renyuan ZHANG - Google 學術搜尋
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STT-BSNN: An in-memory deep binary spiking neural network based on STT-MRAM. VT Nguyen, QK Trinh, R Zhang, Y Nakashima. IEEE Access 9, 151373-151385, 2021. 18 ...
Quang-Kien Trinh (0000-0002-0499-2938)
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STT-BSNN: An In-Memory Deep Binary Spiking Neural Network Based on STT-MRAM ... XNOR-BSNN: In-Memory Computing Model for Deep Binarized Spiking Neural Network.
Domain-Specific STT-MRAM-Based In-Memory Computing
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由 A Yusuf 著作2024被引用 6 次 — The research introduces memory cell and array designs for spintronic PIM, facilitating BNN processing,. IoT binary neural networks, biomedical ...
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A low‐power charge‐based integrate‐and‐fire circuit for ...
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由 QM Duong 著作2023被引用 3 次 — This paper presents a charge-based integrate-and-fire (IF) circuit for in-memory binary spiking neural networks (BSNNs).