Publications

Our publications provide insight into some of our leading-edge research.

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2020

DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Yibo Lin, Zixuan Jiang, Jiaqi Gu, Wuxi Li, Shounak Dhar, Mark Haoxing Ren, Brucek Khailany, David Z. Pan
2021 IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award
Monte Carlo Gradient Quantization
Goncalo Mordido, Matthijs Van keirsbilck, Alex Keller

2019

Instant Quantization of neural networks using Monte Carlo Methods
Gonçalo Mordido, Matthijs Van keirsbilck, Alex Keller
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Yibo Lin, Shounak Dhar, Wuxi Li, Mark Haoxing Ren, Brucek Khailany, David Z. Pan
DAC 2019 Best Paper Award
Rethinking full connectivity in recurrent neural networks
Matthijs Van keirsbilck, Alex Keller, Xiaodong Yang

2018

Machine Learning and Rendering
Alex Keller, Jaroslav Křivánek, Jan Novák, Anton Kaplanyan, Marco Salvi

2017

Parallel Complexity of Forward and Backward Propagation
Maxim Naumov
Integral Equations and Machine Learning
Alex Keller, Ken Dahm
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Parallel Jaccard and Related Graph Clustering Techniques
Alexandre Fender, Nahid Emad, Serge Petiton, Joe Eaton, Maxim Naumov
Parallel Modularity Clustering
Alexandre Fender, Nahid Emad, Serge Petiton, Maxim Naumov

2016

Parallel Spectral Graph Partitioning
Maxim Naumov, Timothy Moon