Research Labs
All Research Labs
3D Deep Learning
Applied Research
Autonomous Vehicles
Deep Imagination
Publications
AI Playground
New and Featured
AI Art Gallery
NGC Demos
Research Areas
AI & Machine Learning
3D Deep Learning
Computer Vision
Robotics
All Areas
Careers
Academic Collaborations
Government Collaborations
Graduate Fellowship
Internships
Research Openings
Research Scientists
Meet the Team
Licensing
Skip to main content
Artificial Intelligence Computing Leadership from NVIDIA
Login
Research Labs
All Research Labs
3D Deep Learning
Applied Research
Autonomous Vehicles
Deep Imagination
Publications
AI Playground
New and Featured
AI Art Gallery
NGC Demos
Research Areas
AI & Machine Learning
3D Deep Learning
Computer Vision
Robotics
All Areas
Careers
Academic Collaborations
Government Collaborations
Graduate Fellowship
Internships
Research Openings
Research Scientists
Meet the Team
Licensing
Search
Search
Enter the terms you wish to search for.
People
Steve Dai
Main Field of Interest
Circuits and VLSI Design
Publications
2023
Efficient Transformer Inference with Statically Structured Sparse Attention
Steve Dai
, Hasan Genc,
Rangharajan Venkatesan
,
Brucek Khailany
2023 60th ACM/IEEE Design Automation Conference (DAC)
A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm
Ben Keller
,
Rangharajan Venkatesan
,
Steve Dai
,
Stephen Tell
,
Brian Zimmer
,
Charbel Sakr
,
William Dally
,
Tom Gray
,
Brucek Khailany
Journal of Solid-State Circuits
2022
LNS-Madam: Low-Precision Training in Logarithmic Number System Using Multiplicative Weight Update
Jiawei Zhao,
Steve Dai
,
Rangharajan Venkatesan
,
Brian Zimmer
, Mustafa Ali,
Ming-Yu Liu
,
Brucek Khailany
,
William Dally
, Anima Anandkumar
IEEE Transactions on Computers (Volume: 71, Issue: 12, 01 December 2022)
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr
,
Steve Dai
,
Rangharajan Venkatesan
,
Brian Zimmer
,
Brucek Khailany
,
William Dally
2022 International Conference on Machine Learning (ICML)
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm
Ben Keller
,
Rangharajan Venkatesan
,
Steve Dai
,
Stephen Tell
,
Brian Zimmer
,
William Dally
,
Tom Gray
,
Brucek Khailany
2022 Symposium on VLSI Technology & Circuits Digest of Technical Papers
2021
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens,
Rangharajan Venkatesan
,
Steve Dai
,
Brucek Khailany
, Anand Raghunathan
Design Automation Conference (DAC) 2021
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
MLSys 2021
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
arXiv
2020
Accelerating Chip Design with Machine Learning
Brucek Khailany
,
Haoxing (Mark) Ren
,
Steve Dai
, Saad Godil,
Ben Keller
, Robert Kirby, Alicia Klinefelter,
Rangharajan Venkatesan
,
Yanqing Zhang
, Bryan Catanzaro,
William Dally
IEEE Micro
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
International Conference On Computer Aided Design (ICCAD)