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07/16/2022
Approximation Capabilities of Neural Networks using Morphological Perceptrons and Generalizations
Standard artificial neural networks (ANNs) use sum-product or multiply-a...
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03/27/2020
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
We present Deep-n-Cheap – an open-source AutoML framework to search for ...
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01/29/2020
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
The high energy cost of processing deep convolutional neural networks im...
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10/22/2019
Neural Network Training with Approximate Logarithmic Computations
The high computational complexity associated with training deep neural n...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolution for Deep CNNs
The high demand for computational and storage resources severely impede ...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolutionfor Deep CNNs
The high demand for computational and storage resources severely impede ...
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12/04/2018
Pre-Defined Sparse Neural Networks with Hardware Acceleration
Neural networks have proven to be extremely powerful tools for modern ar...
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07/11/2018
Morse Code Datasets for Machine Learning
We present an algorithm to generate synthetic datasets of tunable diffic...
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05/31/2018
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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11/06/2017
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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11/03/2017