Publications

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

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2021

Controlling graph dynamics with reinforcement learning and graph neural networks
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, Arash Vahdat
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
Clemens Eppner, Arsalan Mousavian, Dieter Fox
Simba: scaling deep-learning inference with chiplet-based architecture
Yakun Sophia Shao, Jason Clemons, Rangharajan Venkatesan, Brian Zimmer, Matt Fojtik, Ted Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen Tell, Yanqing Zhang, William Dally, Joel Emer, Tom Gray, Brucek Khailany, Steve Keckler
ACM Research Highlight
Contrastive Syn-to-Real Generalization
Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, Jose M. Alvarez, Zhangyang Wang, Anima Anandkumar
GAMMA: Exploiting Gustavson’s Algorithm to Accelerate Sparse Matrix Multiplication
Guowei Zhang, Nithya Attaluri, Joel Emer, Daniel Sanchez
Appearance-Driven Automatic 3D Model Simplification
Jon Hasselgren, Jacob Munkberg, Jaakko Lehtinen, Miika Aittala, Samuli Laine
VS-QUANT: Per-Vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Steve Dai, Rangharajan Venkatesan, Haoxing (Mark) Ren, Brian Zimmer, William Dally, Brucek Khailany
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya
On the Universality of Rotation Equivariant Point Cloud Networks
Nadav Dym, Haggai Maron
Learning the Pareto Front with Hypernetworks
Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik
Learning Sparse Matrix Row Permutations for Efficient SpMM on GPU Architectures
Atefeh Mehrabi, Donghyuk Lee, Niladrish Chatterjee, Danial J. Sorin, Benjamin C. Lee, Mike O'Connor
Robust Vision-Based Cheat Detection in Competitive Gaming
Aditya Jonnalagadda, Iuri Frosio, Seth Schenider, Morgan McGuire, Joohwan Kim
Interactive Path Tracing and Reconstruction of Sparse Volumes
Nikolai Hofmann, Jon Hasselgren, Petrik Clarberg, Jacob Munkberg
Best Paper Presentation, i3D 2021
Deep Learning-based Enhancement of Epigenomics Data with AtacWorks
Avantika Lal, Zachary D Chiang, Nikolai Yakovenko, Fabiana M Duarte, Johnny Israeli, Jason D Buenrostro
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Siva Hari, Michael B. Sullivan, Timothy Tsai, Steve Keckler
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
Hyoukjun Kwon, Liangzhen Lai, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, Vikas Chandra
Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization
Mingjie Liu, Walker Turner, George Kokai, David Z. Pan, Brucek Khailany, Haoxing (Mark) Ren
MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification
Vidya A. Chhabria, Yanqing Zhang, Haoxing (Mark) Ren, Ben Keller, Brucek Khailany, Sachin S. Sapatnekar
Planning and Learning with Adaptive Lookahead
Aviv Rosenberg, Assaf Hallak, Shie Mannor, Gal Chechik, Gal Dalal
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, Sanja Fidler
Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes
Haoxing (Mark) Ren, Matt Fojtik
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Idan Achituve, Haggai Maron, Gal Chechik
From Generalized Zero-Shot Learning to Long-Tail with Class Descriptors
Dvir Samuel, Yuval Atzmon, Gal Chechik
Acting in Delayed Environments with Non-Stationary Markov Policies
Esther Derman, Gal Dalal, Shie Mannor
Flexion: A Quantitative Metric for Flexibility in DNN Accelerators
Hyoukjun Kwon, Michael Pellauer, Angshuman Parashar, Tushar Krishna