Publications

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

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2021

Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan
Graph Learning-Based Arithmetic Block Identification
Zhuolun He, Ziyi Wang, Chen Bai, Haoyu Yang, Bei Yu
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
VS-QUANT: Per-Vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Steve Dai, Rangharajan Venkatesan, Mark Haoxing Ren, Brian Zimmer, William Dally, Brucek Khailany
Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization
Mingjie Liu, Walker Turner, George Kokai, David Z. Pan, Brucek Khailany, Mark Haoxing Ren
MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification
Vidya A. Chhabria, Yanqing Zhang, Mark Haoxing Ren, Ben Keller, Brucek Khailany, Sachin S. Sapatnekar
Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes
Mark Haoxing Ren, Matt Fojtik

2020

NVCell: Generate Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning
Mark Haoxing Ren, Matt Fojtik, Brucek Khailany
Accelerating Chip Design with Machine Learning
Brucek Khailany, Mark Haoxing Ren, Steve Dai, Saad Godil, Ben Keller, Robert Kirby, Alicia Klinefelter, Rangharajan Venkatesan, Yanqing Zhang, Bryan Catanzaro, William Dally
ParaGraph: Layout Parasitics and Device Parameter Prediction using Graph Neural Networks
Mark Haoxing Ren, George Kokai, Walker Turner, Ting-Sheng Ku
GRANNITE: Graph Neural Network Inference for Transferable Power Estimation
Yanqing Zhang, Mark Haoxing Ren, Brucek Khailany
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
A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm
Brian Zimmer, Rangharajan Venkatesan, Yakun Sophia Shao, Jason Clemons, Matt Fojtik, Ted Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen Tell, Yanqing Zhang, William Dally, Joel Emer, Tom Gray, Steve Keckler, Brucek Khailany
JSSC 2020 Best Paper award
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Zhiyao Xie, Guan-Qi Fang, Yu-Hung Huang, Mark Haoxing Ren, Yanqing Zhang, Brucek Khailany, Shao-Yun Fang, Jiang Hu, Yiran Chen, Erick Carvajal Barboza
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
Zhiyao Xie, Mark Haoxing Ren, Brucek Khailany, Ye Sheng, Santosh Santosh, Jiang Hu, Yiran Chen

2019

MAGNet: A Modular Accelerator Generator for Neural Networks
Rangharajan Venkatesan, Sophia Shao, Miaorong Wang, Jason Clemons, Steve Dai, Matt Fojtik, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Yanqing Zhang, Brian Zimmer, William Dally, Joel Emer, Steve Keckler, Brucek Khailany
Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture
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
Best Paper award
IEEE Micro Top Picks in Computer Architecture (Honorable Mention)
A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm
Brian Zimmer, Rangharajan Venkatesan, Sophia Shao, Jason Clemons, Matt Fojtik, Ted Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen Tell, Yanqing Zhang, William Dally, Joel Emer, Tom Gray, Steve Keckler, Brucek Khailany
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
High Performance Graph Convolutional Networks with Applications in Testability Analysis
Yuzhe Ma, Mark Haoxing Ren, Brucek Khailany, Harbinder Sikka, Lijuan Luo, Karthikeyan Natarajan, Bei Yu
PRIMAL: Power Inference using Machine Learning
Yuan Zhou, Mark Haoxing Ren, Yanqing Zhang, Ben Keller, Brucek Khailany, Zhiru Zhang
Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference
Angad S. Rekhi, Brian Zimmer, Nikola Nedovic, Nigxi Liu, Rangharajan Venkatesan, Miaorong Wang, Brucek Khailany, William Dally, Tom Gray
Routability-Driven Macro Placement with Embedded CNN-Based Prediction Model
Yu-Hung Huang, Zhiyao Xie, Guan-Qi Fang, Tao-Chun Yu, Mark Haoxing Ren, Shao-Yun Fang, Yiran Chen, Jiang Hu

2018

RouteNet: Routability Prediction for Mixed-size Designs using Convolutional Neural Network
Zhiyao Xie, Yu-Hung Huang, Guan-Qi Fang, Mark Haoxing Ren, Shao-Yun Fang, Yiran Chen, Hu Jiang