
DeepnCheap: An Automated Search Framework for Low Complexity Deep Learning
We present DeepnCheap – an opensource AutoML framework to search for ...
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Predefined Sparsity for LowComplexity Convolutional Neural Networks
The high energy cost of processing deep convolutional neural networks im...
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Neural Network Training with Approximate Logarithmic Computations
The high computational complexity associated with training deep neural n...
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A Predefined Sparse Kernel Based Convolution for Deep CNNs
The high demand for computational and storage resources severely impede ...
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A Predefined Sparse Kernel Based Convolutionfor Deep CNNs
The high demand for computational and storage resources severely impede ...
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PreDefined Sparse Neural Networks with Hardware Acceleration
Neural networks have proven to be extremely powerful tools for modern ar...
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Morse Code Datasets for Machine Learning
We present an algorithm to generate synthetic datasets of tunable diffic...
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A Highly Parallel FPGA Implementation of Sparse Neural Network Training
We demonstrate an FPGA implementation of a parallel and reconfigurable a...
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Characterizing Sparse Connectivity Patterns in Neural Networks
We propose a novel way of reducing the number of parameters in the stora...
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Accelerating Training of Deep Neural Networks via Sparse Edge Processing
We propose a reconfigurable hardware architecture for deep neural networ...
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Keith M. Chugg
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