8 citations · 18 across the 25 of their papers we have counts for
5 papers · 1 filter
Generating GPU Compiler Heuristics using Reinforcement Learning
Ian Colbert, Jake Daly, Norm Rubin
GPU compilers are complex software programs with many optimizations specific to target hardware. These optimizations are often controlled by heuristics hand-designed by compiler ex…
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado +1
Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the…
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
A novel energy-efficient edge computing paradigm is proposed for real-time deep learning-based image upsampling applications. State-of-the-art deep learning solutions for image ups…
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,…