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
Publications
Our publications provide insight into some of our leading-edge research.
Filters
Search
Apply
Filters
Filters
Publication Year
2023
(2)
2022
(1)
2021
(3)
2019
(2)
Facet Publication Year
Research Areas
Artificial Intelligence and Machine Learning
(8)
Circuits and VLSI Design
(8)
Computer Architecture
(8)
Generative AI
(1)
Events
No Results Available
8 results found
Artificial Intelligence and Machine Learning
Circuits and VLSI Design
Computer Architecture
Clear all
Artificial Intelligence and Machine Learning
Circuits and VLSI Design
Computer Architecture
2023
Efficient Transformer Inference with Statically Structured Sparse Attention
Steve Dai
, Hasan Genc,
Rangharajan Venkatesan
,
Brucek Khailany
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
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
2021
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens,
Rangharajan Venkatesan
,
Steve Dai
,
Brucek Khailany
, Anand Raghunathan
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
,
Haoxing (Mark) Ren
,
Brian Zimmer
,
William Dally
,
Brucek Khailany
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)