Search Results for author: Ken Kreutz-Delgado

Found 11 papers, 4 papers with code

Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations

2 code implementations15 Oct 2021 Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.

Network Pruning Quantization

Tuning Confidence Bound for Stochastic Bandits with Bandit Distance

no code implementations6 Oct 2021 Xinyu Zhang, Srinjoy Das, Ken Kreutz-Delgado

We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others.

An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling

1 code implementation15 Jul 2021 Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

We analyze and compare the inference properties of convolution-based upsampling algorithms using a quantitative model of incurred time and energy costs and show that using deconvolution for inference at the edge improves both system latency and energy efficiency when compared to their sub-pixel or resize convolution counterparts.

Edge-computing

Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework

no code implementations31 Jan 2021 Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data, and limited internet bandwidth for frequent training on cloud servers.

PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems

no code implementations28 Oct 2019 Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado, Alexander Cloninger, Srinjoy Das

Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis.

Denoising Model Selection +1

AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks

no code implementations11 Mar 2019 Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight.

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

1 code implementation22 Jan 2019 Luca Pion-Tonachini, Ken Kreutz-Delgado, Scott Makeig

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution.

Computational Efficiency EEG +1

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

1 code implementation7 May 2017 Xin-Yu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.

General Classification Generative Adversarial Network +6

ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks

no code implementations13 Apr 2017 Xiaojing Xu, Srinjoy Das, Ken Kreutz-Delgado

Probabilistic generative neural networks are useful for many applications, such as image classification, speech recognition and occlusion removal.

General Classification Image Classification +2

Gibbs Sampling with Low-Power Spiking Digital Neurons

no code implementations26 Mar 2015 Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla, John Arthur, Andrew S. Cassidy, Bryan L. Jackson, Dharmendra Modha, Gert Cauwenberghs, Ken Kreutz-Delgado

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition.

General Classification Image Classification +2

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