Results 41 to 50 of about 1,314,055 (250)
Neuron-level fuzzy memoization in RNNs [PDF]
, 2019 The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation.
Arnau Montañés, José María +3 more
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A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021 The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra ...
C. Suarez-Ramirez +4 more
semanticscholar +1 more source
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
doaj +1 more source
Ion beam analysis based on cellular nonlinear networks [PDF]
, 2011 The development of a non- destructive measurement method for ion beam parameters has been treated in various projects. Although results are promising, the high complexity of beam dynamics has made it impossible to implement a real time process control up
Ratzinger, Ulrich +3 more
core +1 more source
BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks
IEEE Access, 2019 Binarization can greatly compress and accelerate deep convolutional neural networks (CNNs) for real-time industrial applications. However, existing binarized CNNs (BCNNs) rely on scaling factor (SF) and batch normalization (BatchNorm) that still involve ...
Lijun Wu +6 more
doaj +1 more source
Memristor Based Binary Convolutional Neural Network Architecture With Configurable Neurons
Frontiers in Neuroscience, 2021 The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition capability.
Lixing Huang +6 more
doaj +1 more source
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks [PDF]
, 2017 Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks.
Cauwenberghs, Gert +3 more
core +2 more sources
OxRAM + OTS optimization for binarized neural network hardware implementation [PDF]
Semiconductor Science and Technology, 2021 Abstract Low-power memristive devices embedded on graphics or central processing units logic core are a very promising non-von-Neumann approach to improve significantly the speed and power consumption of deep learning accelerators, enhancing their deployment on embedded systems.
Minguet Lopez, Joel +13 more
openaire +3 more sources
Low-Overhead Implementation of Binarized Neural Networks Employing Robust 2T2R Resistive RAM Bridges
European Solid-State Circuits Conference, 2021 The energy consumption associated with data movement between memory and processing units is the main roadblock for the massive deployment of edge Artificial Intelligence. To overcome this challenge, Binarized Neural Networks (BNN) coupled with RRAM-based
M. Ezzadeen +7 more
semanticscholar +1 more source
Hardware implementation of RRAM based binarized neural networks
APL Materials, 2019 Resistive switching random access memory (RRAM) has been explored to accelerate the computation of neural networks. RRAM with linear conductance modulation is usually required for the efficient weight updating during the online training according to the ...
Peng Huang +7 more
doaj +1 more source