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Low-Power Adiabatic/MTJ LIM-Based XNOR/XOR Synapse and Neuron for Binarized Neural Networks
2023 IEEE 23rd International Conference on Nanotechnology (NANO), 2023Using binarized neural network (BNN) as an alternative to the conventional convolutional neural network is a promising candidate to answer the demand of using human brain-inspired in applications with limited hardware and power resources, such as ...
Milad Tanavardi Nasab +1 more
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IEEE transactions on applied superconductivity, 2023
For the binary neural network (BNN), We design a max pooling operation circuit (MPOC) using single-flux-quantum circuits based on the multiple data comparators.
Zeyu Han +3 more
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For the binary neural network (BNN), We design a max pooling operation circuit (MPOC) using single-flux-quantum circuits based on the multiple data comparators.
Zeyu Han +3 more
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Toward Accurate Binarized Neural Networks With Sparsity for Mobile Application
IEEE Transactions on Neural Networks and Learning Systems, 2022While binarized neural networks (BNNs) have attracted great interest, popular approaches proposed so far mainly exploit the symmetric $sign$ function for feature binarization, i.e., to binarize activations into −1 and +1 with a fixed threshold of 0 ...
Peisong Wang, Xiangyu He, Jian Cheng
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Optical processor for a binarized neural network
Optics Letters, 2022We propose and experimentally demonstrate an optical processor for a binarized neural network (NN). Implementation of a binarized NN involves multiply-accumulate operations, in which positive and negative weights should be implemented.
Long Huang, Jianping Yao
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JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits
IEEE transactions on computers, 2022As a high-performance application of low-temperature superconductivity, superconducting single-flux-quantum (SFQ) circuits have high speed and low-power consumption characteristics, which have recently received extensive attention, especially in the ...
Rongliang Fu +5 more
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An Efficient Channel-Aware Sparse Binarized Neural Networks Inference Accelerator
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2022The binarized neural network (BNN) inference accelerators show great promise in cost- and power-restricted domains. However, the performances of these accelerators are still severely limited by the significant redundancies in BNNs inference.
Qingliang Liu, Jinmei Lai, Jiabao Gao
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SBNN: Slimming binarized neural network
Neurocomputing, 2020Abstract With the rapid developments of deep neural networks related applications, approaches for accelerating computationally intensive convolutional neural networks, such as network quantization, pruning, knowledge distillation, have attracted ever-increasing attention.
Qing Wu +5 more
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Binarized Attributed Network Embedding via Neural Networks
2020 International Joint Conference on Neural Networks (IJCNN), 2020Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit ...
Hangyu Xia +4 more
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IEEE transactions on applied superconductivity, 2022
We design a binary convolution operation circuit (BCOC) using a single-flux-quantum circuit for high-speed and energy-efficient neural network. The proposed circuit is used for binary convolution operations using a convolution kernel size of 3 $ \times $
Zongyuan Li, Y. Yamanashi, N. Yoshikawa
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We design a binary convolution operation circuit (BCOC) using a single-flux-quantum circuit for high-speed and energy-efficient neural network. The proposed circuit is used for binary convolution operations using a convolution kernel size of 3 $ \times $
Zongyuan Li, Y. Yamanashi, N. Yoshikawa
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Weight Compression-Friendly Binarized Neural Network
2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 2020The resources of edge devices in AIoT systems are usually constrained with size and power. The computational complexity of neural network models in these edge devices has become a major concern. The most compact form of deep neural networks is binarized neural network (BNN), which adopts binary weights and exclusive NOR (XNOR) operations as binary ...
Yuzhong Jiao +4 more
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