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Found 5 papers, 1 papers with code
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
Efficient Inference of CNNs via Channel Pruning
The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in compressing and accelerating trained CNN models.
Design Rule Violation Hotspot Prediction Based on Neural Network Ensembles
Design rule check is a critical step in the physical design of integrated circuits to ensure manufacturability.
A Mixture of Expert Approach for Low-Cost Customization of Deep Neural Networks
Since the customized dataset is in general very small, the cost of training LE and GN would be much lower than that of re-training of GE.
