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11/27/2021
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility ...
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10/15/2021
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Quantization and pruning are core techniques used to reduce the inferenc...
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10/06/2021
Tuning Confidence Bound for Stochastic Bandits with Bandit Distance
We propose a novel modification of the standard upper confidence bound (...
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07/15/2021
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
A novel energy-efficient edge computing paradigm is proposed for real-ti...
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01/31/2021
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
The use of Deep Learning hardware algorithms for embedded applications i...
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01/30/2021
A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?
When trained as generative models, Deep Learning algorithms have shown e...
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10/28/2019
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted...
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03/11/2019
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
The power budget for embedded hardware implementations of Deep Learning ...
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01/22/2019
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
The electroencephalogram (EEG) provides a non-invasive, minimally restri...
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05/07/2017
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
In recent years deep learning algorithms have shown extremely high perfo...
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04/13/2017
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks
Probabilistic generative neural networks are useful for many application...
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03/26/2015
