no code implementations • 27 Nov 2021 • Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
2 code implementations • 15 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.
no code implementations • 6 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.
1 code implementation • 15 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.
no code implementations • 31 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.
no code implementations • 28 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.
no code implementations • 11 Mar 2019 • Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight.
1 code implementation • 22 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.
1 code implementation • 7 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.
no code implementations • 13 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.
no code implementations • 26 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.