Results 11 to 20 of about 1,721,209 (309)
Binary neural networks: A survey [PDF]
Pattern Recognition, 2020 The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety
Haotong Qin +5 more
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A Systematic Literature Review on Binary Neural Networks
IEEE Access, 2023 This paper presents an extensive literature review on Binary Neural Network (BNN). BNN utilizes binary weights and activation function parameters to substitute the full-precision values.
Ratshih Sayed +4 more
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AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets [PDF]
European Conference on Computer Vision, 2022 This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights ...
Zhijun Tu +3 more
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Recurrent Bilinear Optimization for Binary Neural Networks [PDF]
European Conference on Computer Vision, 2022 Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an essential role in reducing the performance gap to their real-valued counterparts.
Xu, Sheng +8 more
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ReCU: Reviving the Dead Weights in Binary Neural Networks [PDF]
IEEE International Conference on Computer Vision, 2021 Binary neural networks (BNNs) have received increasing attention due to their superior reductions of computation and memory. Most existing works focus on either lessening the quantization error by minimizing the gap between the full-precision weights and
Zihan Xu +7 more
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Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition [PDF]
arXiv.org, 2023 Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.
J. Carrasquilla +6 more
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BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Deep neural networks, such as the deep-FSMN, have been widely studied for keyword spotting (KWS) applications while suffering expensive computation and storage.
Haotong Qin +8 more
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Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks [PDF]
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2022 Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as a human-computer interface.
G. Cerutti +5 more
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Malware Detection Using Binary Visualization and Neural Networks [PDF]
E3S Web of Conferences, 2023 Any programme or code that is damaging to our systems or networks is known as Malware or malicious software. Malware attempts to infiltrate, damage, or destroy our gadgets such as computers, networks, tablets, and so on. Malware may also grant partial or
Jonnala Yamini Devi +4 more
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“BNN - BN = ?”: Training Binary Neural Networks without Batch Normalization [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021 Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the ...
Tianlong Chen +5 more
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