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

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

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

Minimum precision requirements for the SVM-SGD learning algorithm. - Charbel Sakr; Ameya D. Patil; Sai Zhang; Yongjune Kim 001; Naresh R. Shanbhag (ICASSP, 2017)

Analytical Guarantees on Numerical Precision of Deep Neural Networks. - Charbel Sakr; Yongjune Kim 001; Naresh R. Shanbhag (ICML, 2017)

PredictiveNet: An energy-efficient convolutional neural network via zero prediction. - Yingyan Lin; Charbel Sakr; Yongjune Kim 001; Naresh R. Shanbhag (ISCAS, 2017)

Minimum Precision Requirements for Deep Learning with Biomedical Datasets. - Charbel Sakr; Naresh R. Shanbhag (BioCAS, 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 (ICASSP, 2018)

An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks. - Charbel Sakr; Naresh R. Shanbhag (ICASSP, 2018)

Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. - Charbel Sakr; Naresh R. Shanbhag (ICLR (Poster), 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 (ICLR (Poster), 2019)

Minimum Precision Requirements of General Margin Hyperplane Classifiers. - Charbel Sakr; Yongjune Kim 001; Naresh R. Shanbhag (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

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 (CICC, 2020)

Fundamental Limits on the Precision of In-memory Architectures. - Sujan K. Gonugondla; Charbel Sakr; Hassan Dbouk; Naresh R. Shanbhag (ICCAD, 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 001; Michael B. Sullivan 001; Timothy Tsai 002; Stephen W. Keckler (CoRR, 2020)

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 001; Michael B. Sullivan 001; Timothy Tsai 002; Stephen W. Keckler (ISSRE, 2021)

A 0.44-μJ/dec, 39.9-μs/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)

Finite precision deep learning with theoretical guarantees - Charbel Sakr (2021)

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

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)

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)

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)

ESPACE: Dimensionality Reduction of Activations for Model Compression. - Charbel Sakr; Brucek Khailany (NeurIPS, 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 001; Min-Hung Chen (CoRR, 2024)

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)