Results 31 to 40 of about 36,322 (258)
Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems
Frontiers in Neuroscience, 2020 To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems ...
Wenzhe Guo +7 more
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To what extent is tuned neural network pruning beneficial in software effort estimation? [PDF]
Computer Science Journal of Moldova, 2021 Software effort estimation (SEE) is of great importance for planning the budgets of future projects. The models of SEE are developed depending on the enhancements of hardware technology. However, developing such models based on neural networks remarkably
Muhammed Maruf Ozturk
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Depth Pruning with Auxiliary Networks for Tinyml
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely constrained environments in tinyML. Unfortunately, special hardware requirements and limited study on its effectiveness on already compact models prevent its wider adoption ...
Josen Daniel De Leon, Rowel Atienza
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CupNet – Pruning a Network for Geometric Data [PDF]
, 2021 4 pages, 2 figures, 1 ...
Raoul Heese +3 more
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Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain
Frontiers in Systems Neuroscience, 2021 Most neural networks need to predefine the network architecture empirically, which may cause over-fitting or under-fitting. Besides, a large number of parameters in a fully connected network leads to the prohibitively expensive computational cost and ...
Feifei Zhao +6 more
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Partition Pruning: Parallelization-Aware Pruning for Dense Neural Networks [PDF]
2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020 Parameters of recent neural networks require a huge amount of memory. These parameters are used by neural networks to perform machine learning tasks when processing inputs. To speed up inference, we develop Partition Pruning, an innovative scheme to reduce the parameters used while taking into consideration parallelization. We evaluated the performance
Shahhosseini, Sina +3 more
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Quantization Robust Pruning With Knowledge Distillation
IEEE Access, 2023 To resolve the problem that deep neural networks (DNN) require a large number of network parameters, many researchers have sought to compress the network.
Jangho Kim
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PLoS Computational Biology, 2021 During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning.
Carolin Scholl +2 more
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Structural Pruning Algorithm Based on Second-Order Information of Deep Neural Network [PDF]
Jisuanji gongcheng, 2021 Most of the existing structural pruning algorithms are based on the first-order or zero-order information of Deep Neural Network(DNN).To use the second-order information of the networks for speeding up the convergence of DNN models,this paper proposes a ...
JI Fanfan, YANG Xin, YUAN Xiaotong
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Automatic Channel Pruning Method Based on Zebra Optimization Algorithm [PDF]
Jisuanji gongchengThe high computational and storage requirements of Convolutional Neural Networks (CNNs) limit their application in resource-limited mobile edge devices. Model compression techniques can significantly reduce the computational effort and parameters of CNNs
LIU Yajun, WU Dakui, FAN Kefeng, ZHOU Wenju
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