18 citations · 73 across the 14 of their papers we have counts for
7 papers · 1 filter
Approximation Capabilities of Neural Networks using Morphological Perceptrons and Generalizations
William Chang, Hassan Hamad, Keith M. Chugg
Standard artificial neural networks (ANNs) use sum-product or multiply-accumulate node operations with a memoryless nonlinear activation. These neural networks are known to have un…
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
Sourya Dey, Saikrishna C. Kanala, Keith M. Chugg +1
We present Deep-n-Cheap -- an open-source AutoML framework to search for deep learning models. This search includes both architecture and training hyperparameters, and supports con…
Neural Network Training with Approximate Logarithmic Computations
Arnab Sanyal, Peter A. Beerel, Keith M. Chugg
The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and…
Pre-Defined Sparse Neural Networks with Hardware Acceleration
Sourya Dey, Kuan-Wen Huang, Peter A. Beerel +1
Neural networks have proven to be extremely powerful tools for modern artificial intelligence applications, but computational and storage complexity remain limiting factors. This p…
Morse Code Datasets for Machine Learning
Sourya Dey, Keith M. Chugg, Peter A. Beerel
We present an algorithm to generate synthetic datasets of tunable difficulty on classification of Morse code symbols for supervised machine learning problems, in particular, neural…
Interleaver Design for Deep Neural Networks
Sourya Dey, Peter A. Beerel, Keith M. Chugg
We propose a class of interleavers for a novel deep neural network (DNN) architecture that uses algorithmically pre-determined, structured sparsity to significantly lower memory an…