BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference - Reena Elangovan; Charbel Sakr; Anand Raghunathan; Brucek Khailany (CoRR, 2025)

FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference - Coleman Hooper; Charbel Sakr; Ben Keller; Rangharajan Venkatesan; Kurt Keutzer; Yakun Sophia Shao; Brucek Khailany (CoRR, 2025)

Pretraining Large Language Models with NVFP4 - Felix Abecassis; Anjulie Agrusa; Dong Ahn; Jonah Alben; Stefania Alborghetti; Michael Andersch; Sivakumar Arayandi; Alexis Bjorlin; Aaron Blakeman; Evan Briones; Ian Buck; Bryan Catanzaro; et al. (CoRR, 2025)

ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models - Akshat Ramachandran; Marina Neseem; Charbel Sakr; Rangharajan Venkatesan; Brucek Khailany; Tushar Krishna (CoRR, 2025)

ESPACE: Dimensionality Reduction of Activations for Model Compression - Charbel Sakr; Brucek Khailany (Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024, 2024)

ESPACE: Dimensionality Reduction of Activations for Model Compression - Charbel Sakr; Brucek Khailany (CoRR, 2024)

EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation - Shih-Yang Liu; Huck Yang; Chein-Yi Wang; Nai Chit Fung; Hongxu Yin; Charbel Sakr; Saurav Muralidharan; Kwang-Ting Cheng; Jan Kautz; Yu-Chiang Frank Wang; Pavlo Molchanov; Min-Hung Chen (CoRR, 2024)

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 G. Tell; Brian Zimmer; Charbel Sakr; William J. Dally; C. Thomas Gray; Brucek Khailany (IEEE J. Solid State Circuits, 2023)

VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning - Yu-Shun Hsiao; Siva Kumar Sastry Hari; Balakumar Sundaralingam; Jason Yik; Thierry Tambe; Charbel Sakr; Stephen W. Keckler; Vijay Janapa Reddi (IROS, 2023)

VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning - Yu-Shun Hsiao; Siva Kumar Sastry Hari; Balakumar Sundaralingam; Jason Yik; Thierry Tambe; Charbel Sakr; Stephen W. Keckler; Vijay Janapa Reddi (CoRR, 2023)

Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications - Sujan K. Gonugondla; Charbel Sakr; Hassan Dbouk; Naresh R. Shanbhag (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training - Charbel Sakr; Steve Dai; Rangharajan Venkatesan; Brian Zimmer; William J. Dally; Brucek Khailany (International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, 2022)

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training - Charbel Sakr; Steve Dai; Rangharajan Venkatesan; Brian Zimmer; William J. Dally; Brucek Khailany (CoRR, 2022)

Finite precision deep learning with theoretical guarantees - Charbel Sakr (University of Illinois Urbana-Champaign, USA, 2021)

A 0.44-\(μ\)J/dec, 39.9-\(μ\)s/dec, Recurrent Attention In-Memory Processor for Keyword Spotting - Hassan Dbouk; Sujan K. Gonugondla; Charbel Sakr; Naresh R. Shanbhag (IEEE J. Solid State Circuits, 2021)

Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures - Charbel Sakr; Naresh R. Shanbhag (IEEE Trans. Signal Process., 2021)

Optimizing Selective Protection for CNN Resilience - Abdulrahman Mahmoud; Siva Kumar Sastry Hari; Christopher W. Fletcher; Sarita V. Adve; Charbel Sakr; Naresh R. Shanbhag; Pavlo Molchanov; Michael B. Sullivan; Timothy Tsai; Stephen W. Keckler (32nd IEEE International Symposium on Software Reliability Engineering, ISSRE 2021, Wuhan, China, October 25-28, 2021, 2021)

KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting - Hassan Dbouk; Sujan K. Gonugondla; Charbel Sakr; Naresh R. Shanbhag (2020 IEEE Custom Integrated Circuits Conference, CICC 2020, Boston, MA, USA, March 22-25, 2020, 2020)

Fundamental Limits on the Precision of In-memory Architectures - Sujan K. Gonugondla; Charbel Sakr; Hassan Dbouk; Naresh R. Shanbhag (IEEE/ACM International Conference On Computer Aided Design, ICCAD 2020, San Diego, CA, USA, November 2-5, 2020, 2020)

HarDNN: Feature Map Vulnerability Evaluation in CNNs - Abdulrahman Mahmoud; Siva Kumar Sastry Hari; Christopher W. Fletcher; Sarita V. Adve; Charbel Sakr; Naresh R. Shanbhag; Pavlo Molchanov; Michael B. Sullivan; Timothy Tsai; Stephen W. Keckler (CoRR, 2020)

Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications - Sujan Kumar Gonugondla; Charbel Sakr; Hassan Dbouk; Naresh R. Shanbhag (CoRR, 2020)

Minimum Precision Requirements of General Margin Hyperplane Classifiers - Charbel Sakr; Yongjune Kim; Naresh R. Shanbhag (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm - Charbel Sakr; Naresh R. Shanbhag (7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019)

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks - Charbel Sakr; Naigang Wang; Chia-Yu Chen; Jungwook Choi; Ankur Agrawal; Naresh R. Shanbhag; Kailash Gopalakrishnan (7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019)

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks - Charbel Sakr; Naigang Wang; Chia-Yu Chen; Jungwook Choi; Ankur Agrawal; Naresh R. Shanbhag; Kailash Gopalakrishnan (CoRR, 2019)

Minimum Precision Requirements for Deep Learning with Biomedical Datasets - Charbel Sakr; Naresh R. Shanbhag (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018, Cleveland, OH, USA, October 17-19, 2018, 2018)

An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks - Charbel Sakr; Naresh R. Shanbhag (2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018, 2018)

True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization - Charbel Sakr; Jungwook Choi; Zhuo Wang; Kailash Gopalakrishnan; Naresh R. Shanbhag (2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018, 2018)

Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm - Charbel Sakr; Naresh R. Shanbhag (CoRR, 2018)

Minimum precision requirements for the SVM-SGD learning algorithm - Charbel Sakr; Ameya D. Patil; Sai Zhang; Yongjune Kim; Naresh R. Shanbhag (2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5-9, 2017, 2017)

Analytical Guarantees on Numerical Precision of Deep Neural Networks - Charbel Sakr; Yongjune Kim; Naresh R. Shanbhag (Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, 2017)

PredictiveNet: An energy-efficient convolutional neural network via zero prediction - Yingyan Lin; Charbel Sakr; Yongjune Kim; Naresh R. Shanbhag (IEEE International Symposium on Circuits and Systems, ISCAS 2017, Baltimore, MD, USA, May 28-31, 2017, 2017)

Reducing the Energy Cost of Inference via In-sensor Information Processing - Sai Zhang; Mingu Kang; Charbel Sakr; Naresh R. Shanbhag (CoRR, 2016)

Understanding the Energy and Precision Requirements for Online Learning - Charbel Sakr; Ameya Patil; Sai Zhang; Naresh R. Shanbhag (CoRR, 2016)

Carrier-Sense Protocols for Packet-Switched Smart Antenna Basestations - Charbel Sakr; Terence D. Todd (Int. J. Wirel. Inf. Networks, 2000)

Carrier-sense protocols for packet-switched smart antenna basestations - Charbel Sakr; Terence D. Todd (1997 International Conference on Network Protocols (ICNP '97), 28-31 October 1997, Atlanta, GA, USA, 1997)