Rangharajan Venkatesan

Nvidia (United States) — United States

Engineering · Physical Sciences

24h-index4.0kcitations70works0.72yr avg
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Research Topics

Advanced Memory and Neural Computing(25), Ferroelectric and Negative Capacitance Devices(20), Advanced Neural Network Applications(17), Magnetic properties of thin films(16), Advanced Data Storage Technologies(14)

Publications70 total

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity
2025
SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity
arXiv (Cornell University)·2025Open Access
Fusing Deep Learning Techniques for Intrusion Detection in Smart Grids
Fusion Practice and Applications·2024· 2 cited
Efficient Transformer Inference with Statically Structured Sparse Attention
2023· 7 cited
Rule-Based Outlier Detection with a Modified Variational AutoEncoder for Enhancing Data Accuracy in Wireless Sensor Networks
International Journal of Fuzzy Systems·2023· 7 cited
Session 22 Overview: Heterogenous ML Accelerators
2023Open Access
A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm
IEEE Journal of Solid-State Circuits·2023· 44 cited
LNS-Madam: Low-Precision Training in Logarithmic Number System Using Multiplicative Weight Update
IEEE Transactions on Computers·2022· 19 citedOpen Access
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)·2022· 40 cited
F5: How to Improve AI Efficiency Further: New Devices, Architectures and Algorithms
2022 IEEE International Solid- State Circuits Conference (ISSCC)·2022Open Access
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
2021· 2 citedOpen Access
IPA: Floorplan-Aware SystemC Interconnect Performance Modeling and Generation for HLS-based SoCs
2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)·2021
Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
CaltechAUTHORS (California Institute of Technology)·2021· 3 citedOpen Access
Simba
Communications of the ACM·2021· 19 citedOpen Access
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Proceedings of Machine Learning and Systems·2021· 2 cited
Fair and Comprehensive Benchmarking of Machine Learning Processing Chips
IEEE Design and Test·2021· 24 cited
Session 3 Overview: Highlighted Chip Releases: Modern Digital SoCs
2021Open Access
Tutorial: Basic Design Approaches to Accelerating Deep Neural Networks
2021
Verifying High-Level Latency-Insensitive Designs with Formal Model Checking
arXiv (Cornell University)·2021· 2 citedOpen Access
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
arXiv (Cornell University)·2021· 13 citedOpen Access
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Frequent Co-authors

Anand Raghunathan(24), Brucek Khailany(22), Kaushik Roy(22), William J. Dally(15), Joel Emer(13), Brian Zimmer(13), Stephen W. Keckler(11), Yakun Sophia Shao(10), Jason Clemons(9), Ben Keller(9), Alicia Klinefelter(9), Steve Dai(9), Matthew Fojtik(8), Nathaniel Pinckney(8), Yanqing Zhang(8), C. Thomas Gray(8), Priyanka Raina(7), Stephen G. Tell(7), Mrigank Sharad(6), Xuanyao Fong(5)