8 citations · 8 across the 4 of their papers we have counts for
4 papers
A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?
Ian Colbert, Jake Daly, Ken Kreutz-Delgado +1
When trained as generative models, Deep Learning algorithms have shown exceptional performance on tasks involving high dimensional data such as image denoising and super-resolution…
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado +1
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,…
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado +2
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, im…
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight. To address implementation challenges in such domains, new design paradigm…