Publications

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

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  • 151 results found

2021

Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Siva Hari, Michael B. Sullivan, Timothy Tsai, Steve Keckler
Fair and Comprehensive Benchmarking of Machine Learning Processing Chips
Geoffrey W. Burr, SukHwan Lim, Boris Murmann, Rangharajan Venkatesan, Marian Verhelst
Reduced Precision DWC: An Efficient Hardening Strategy for Mixed-Precision Architectures
Fernando F. dos Santos, Marcelo Brandalero, Michael B. Sullivan, Pedro M. Basso, Michael Hubner, Luigi Carro, Paolo Rech
P-OPT: Practical Optimal Cache Replacement for Graph Analytics
Vignesh Balaji, Neal Crago, Aamer Jaleel, Brandon Lucia
Best Paper nominee
Need for Speed: Experiences Building a Trustworthy System-Level GPU Simulator.
Oreste Villa, Daniel Lustig, Zi Yan, Evgeny Bolotin, Yaosheng Fu, Niladrish Chatterjee, Ted Jiang, David Nellans
Scaling Implicit Parallelism via Dynamic Control Replication
Michael Bauer, Wonchan Lee, Elliott Slaughter, Zhihao Jia, Mario Di Renzo, Manolis Papadakis, Galen Shipman, Patrick McCormick, Michael Garland, Alex Aiken
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
Hyoukjun Kwon, Liangzhen Lai, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, Vikas Chandra
Verifying High-Level Latency-Insensitive Designs with Formal Model Checking
Steve Dai, Alicia Klinefelter, Haoxing (Mark) Ren, Rangharajan Venkatesan, Ben Keller, Nathaniel Pinckney, Brucek Khailany
Transferable Task Execution from Pixels through Deep Planning Domain Learning
Kei Kase, Chris Paxton, Hammad Mazhar, Tetsuya Ogata, Dieter Fox
Ground-Referenced Single-Ended Signaling: Applications for High-Density Short-Haul Communication Systems
John Wilson, Walker Turner, John Poulton
SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference
Jie-Fang Zhang, Ching-En Lee, Chester Liu, Yakun Sophia Shao, Steve Keckler, Zhengya Zhang
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
Optimizing Image Quality for Holographic Near-eye Displays with Michelson Holography
Suyeon Choi, Jonghyun Kim, Yifan Peng, Gordon Wetzstein
Planning and Learning with Adaptive Lookahead
Aviv Rosenberg, Assaf Hallak, Shie Mannor, Gal Chechik, Gal Dalal
Hardware Abstractions for Targeting EDDO Architectures with the Polyhedral Model
Angshuman Parashar, Prasanth Chatarasi, Po-An Tsai
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
Data-Free Knowledge Distillation for Object Detection
Akshay Chawla, Hongxu Danny Yin, Pavlo Molchanov, Jose M. Alvarez
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