1k citations · 1.2k across the 5 of their papers we have counts for
5 papers
EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure
Magnus Själander, Magnus Jahre, Gunnar Tufte +1
The pursuit of many research questions requires massive computational resources. State-of-the-art research in physical processes using simulations, the training of neural networks…
Streamlined Deployment for Quantized Neural Networks
Yaman Umuroglu, Magnus Jahre
Running Deep Neural Network (DNN) models on devices with limited computational capability is a challenge due to large compute and memory requirements. Quantized Neural Networks (QN…
Scaling Binarized Neural Networks on Reconfigurable Logic
Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella +4
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suit…
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella +4
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced…
A Study of Energy and Locality Effects using Space-filling Curves
Nico Reissmann, Jan Christian Meyer, Magnus Jahre
The cost of energy is becoming an increasingly important driver for the operating cost of HPC systems, adding yet another facet to the challenge of producing efficient code. In thi…