PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution [PDF]
PeerJ Computer Science, 2022 In this study, we present a novel performance-enhancing binarized neural network model called PresB-Net: Parametric Binarized Neural Network. A binarized neural network (BNN) model can achieve fast output computation with low hardware costs by using ...
Jungwoo Shin, HyunJin Kim
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sEMG-Based Hand Gesture Recognition Using Binarized Neural Network [PDF]
Sensors, 2023 Recently, human–machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and ...
Soongyu Kang +5 more
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Pre-Computing Batch Normalisation Parameters for Edge Devices on a Binarized Neural Network [PDF]
Sensors, 2023 Binarized Neural Network (BNN) is a quantized Convolutional Neural Network (CNN), reducing the precision of network parameters for a much smaller model size. In BNNs, the Batch Normalisation (BN) layer is essential.
Nicholas Phipps +3 more
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Binarized Neural Network with Silicon Nanosheet Synaptic Transistors for Supervised Pattern Classification [PDF]
Scientific Reports, 2019 In the biological neural network, the learning process is achieved through massively parallel synaptic connections between neurons that can be adjusted in an analog manner.
Sungho Kim +6 more
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Binarized neural network of diode array with high concordance to vector–matrix multiplication [PDF]
Scientific ReportsIn this study, a binarized neural network (BNN) of silicon diode arrays achieved vector–matrix multiplication (VMM) between the binarized weights and inputs in these arrays.
Yunwoo Shin, Kyoungah Cho, Sangsig Kim
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Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network [PDF]
Scientific Reports, 2019 Brain-inspired neuromorphic systems (hardware neural networks) are expected to be an energy-efficient computing architecture for solving cognitive tasks, which critically depend on the development of reliable synaptic weight storage (i.e., synaptic ...
Sungho Kim, Hee-Dong Kim, Sung-Jin Choi
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Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell [PDF]
Nature CommunicationsMemristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters.
Fadi Jebali +15 more
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AresB-Net: accurate residual binarized neural networks using shortcut concatenation and shuffled grouped convolution [PDF]
PeerJ Computer Science, 2021 This article proposes a novel network model to achieve better accurate residual binarized convolutional neural networks (CNNs), denoted as AresB-Net.
HyunJin Kim
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Hardware Platform-Aware Binarized Neural Network Model Optimization
Applied Sciences, 2022 Deep Neural Networks (DNNs) have shown superior accuracy at the expense of high memory and computation requirements. Optimizing DNN models regarding energy and hardware resource requirements is extremely important for applications with resource ...
Quang Hieu Vo +4 more
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Binarized Encoder-Decoder Network and Binarized Deconvolution Engine for Semantic Segmentation
IEEE Access, 2021 Recently, semantic segmentation based on deep neural network (DNN) has attracted attention as it exhibits high accuracy, and many studies have been conducted on this.
Hyunwoo Kim +4 more
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