18 citations · 73 across the 14 of their papers we have counts for
16 papers
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…
Improved Analysis of Current-Steering DACs Using Equivalent Timing Errors
Daniel Beauchamp, Keith M. Chugg
Current-steering (CS) digital-to-analog converters (DACs) generate analog signals by combining weighted current sources. Ideally, the current sources are combined at each switching…
Linearization for High-Speed Current-Steering DACs Using Neural Networks
Daniel Beauchamp, Keith M. Chugg
This paper proposes a novel foreground linearization scheme for a high-speed CS-DAC. The technique leverages neural networks (NNs) to derive a LUT that maps the inverse of the DAC…
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…
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
Souvik Kundu, Mahdi Nazemi, Massoud Pedram +2
The high energy cost of processing deep convolutional neural networks impedes their ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT devices.…
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…