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MoTe<sub>2</sub> synaptic transistor and its application to physical reservoir computing. [PDF]
RSC AdvOh WS, Gim S, Jeong H, Baek H, Oh H.
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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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Detecting Network Intrusion Using Binarized Neural Networks
2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 2021The rise of the internet of things will further increase the number of connected devices, which will provide a new attack surface for compromising computer networks. Network intrusion detection systems have already been proposed to help solve this problem, but they are typically computationally too expensive to run on embedded or IoT devices.
Vreca, Jure +3 more
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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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Partially binarized neural networks for efficient spike sorting
Biomedical Engineering Letters, 2022While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural
Daniel Valencia, Amir Alimohammad
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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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Approximating Binarization in Neural Networks
2019 International Joint Conference on Neural Networks (IJCNN), 2019Binarization of neural networks’ activations may be a requirement for some applications. A typical example is end-to-end learned deep image compression systems where the encoder’s output is requred to be a binary vector. Binarization is non-differentiable, therefore one needs to approximate it in order to train neural networks with stochastic gradient ...
Caglar Aytekin +4 more
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ECG signal classification with binarized convolutional neural network
Computers in Biology and Medicine, 2020Arrhythmias are a group of common conditions associated with irregular heart rhythms. Some of these conditions, for instance, atrial fibrillation (AF), might develop into serious syndromes if not treated in time. Therefore, for high-risk patients, early detection of arrhythmias is crucial.
Qing Wu +3 more
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