Results 21 to 30 of about 22,800 (222)
B-VGG16: Binary Quantized Convolutional Neuronal Network for image classification
Revista Elektrón, 2022 In this work, a Binary Quantized Convolution neural network for image classification is trained and evaluated. Binarized neural networks reduce the amount of memory, and it is possible to implement them with less hardware than those that use real value ...
Nicolás Urbano Pintos +2 more
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Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing
Frontiers in Neuroscience, 2022 With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible at the edge.
Vivek Parmar +3 more
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An Efficient Ensemble Binarized Deep Neural Network on Chip with Perception-Control Integrated
Sensors, 2021 Lightweight UAVs equipped with deep learning models have become a trend, which can be deployed for automatic navigation in a wide range of civilian and military missions.
Wei He +4 more
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CodNN – Robust Neural Networks From Coded Classification [PDF]
, 2020 Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known that DNNs are highly sensitive to noise, whether adversarial or random ...
Bruck, Jehoshua +4 more
core +1 more source
Frontiers in Applied Mathematics and Statistics, 2020 Sparsification of neural networks is one of the effective complexity reduction methods to improve efficiency and generalizability. Binarized activation offers an additional computational saving for inference.
Thu Dinh, Jack Xin
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Sparsity-Inducing Binarized Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Binarization of feature representation is critical for Binarized Neural Networks (BNNs). Currently, sign function is the commonly used method for feature binarization. Although it works well on small datasets, the performance on ImageNet remains unsatisfied.
Peisong Wang +4 more
openaire +2 more sources
Sensors, 2023 Keyword spotting (KWS) systems are used for human–machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks.
Seongwoo Bae +3 more
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Domain Wall Memory-Based Design of Deep Neural Network Convolutional Layers
IEEE Access, 2020 In the hardware implementation of deep learning algorithms such as, convolutional neural networks (CNNs) and binarized neural networks (BNNs), multiple dot products and memories for storing parameters take a significant portion of area and power ...
Jinil Chung +3 more
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A Review of Binarized Neural Networks [PDF]
Electronics, 2019 In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks that use binary values for activations and weights, instead of full precision values. With binary values, BNNs can execute computations using bitwise operations, which reduces execution time.
Taylor Simons, Dah-Jye Lee
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Alterations of brain activity in patients with alcohol use disorder: a resting-state fMRI study
BMC Psychiatry, 2023 Background Alcohol use disorder (AUD) has a negative impact on one’s health and wastes a lot of societal resources since it damages one’s brain tissue. Yet the knowledge of the neural mechanisms underlying alcohol addiction still remains limited.
Xia Ruan +5 more
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