Publications

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

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2023

Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes
Anqi Gong, Sebastian Cammerer, Joseph M. Renes

2022

Deep Learning-Based Synchronization for Uplink NB-IoT
Fayçal Aït Aoudia , Jakob Hoydis, Sebastian Cammerer, Matthijs Van keirsbilck, Alex Keller
Graph Neural Networks for Channel Decoding
Sebastian Cammerer, Jakob Hoydis, Fayçal Aït Aoudia , Alex Keller
Unbiased Inverse Volume Rendering With Differential Trackers
Merlin Nimier-David, Thomas Müller, Alex Keller, Wenzel Jakob
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Brucek Khailany, William Dally
Adaptive Neural Network-based OFDM Receivers
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer, Takayuki Shimizu, Hongsheng Lu, Stephan ten Brink
MatBuilder: Mastering Sampling Uniformity Over Projections
Loïs Paulin, Nicolas Bonneel, David Coeurjolly, Jean-Claude Iehl, Alex Keller, Victor Ostromoukhov
Artificial Neural Networks generated by Low Discrepancy Sequences
Matthijs Van keirsbilck, Alex Keller
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Jakob Hoydis, Sebastian Cammerer, Fayçal Aït Aoudia , Avinash Vem, Nikolaus Binder, Guillermo Marcus, Alex Keller
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing (Mark) Ren

2021

Graph Learning-Based Arithmetic Block Identification
Zhuolun He, Ziyi Wang, Chen Bai, Haoyu Yang, Bei Yu
Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices
Goncalo Mordido, Matthijs Van keirsbilck, Alex Keller

2020

DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Yibo Lin, Zixuan Jiang, Jiaqi Gu, Wuxi Li, Shounak Dhar, Haoxing (Mark) 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, Haoxing (Mark) 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