Charbel Sakr

According to our database, Charbel Sakr authored at least 25 papers between 2000 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

ESPACE: Dimensionality Reduction of Activations for Model Compression.

Charbel Sakr, Brucek Khailany

CoRR, 2024

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

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

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

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training.

Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, William J. Dally, Brucek Khailany

Proceedings of the International Conference on Machine Learning, 2022

2021

Finite precision deep learning with theoretical guarantees

Charbel Sakr

PhD thesis, 2021

Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures.

Charbel Sakr, Naresh R. Shanbhag

IEEE Trans. Signal Process., 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

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

Proceedings of the 32nd IEEE International Symposium on Software Reliability Engineering, 2021

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 the Precision of In-memory Architectures.

Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag

Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2020

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

Proceedings of the 2020 IEEE Custom Integrated Circuits Conference, 2020

2019

Minimum Precision Requirements of General Margin Hyperplane Classifiers.

Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag

IEEE J. Emerg. Sel. Topics Circuits Syst., 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

Proceedings of the 7th International Conference on Learning Representations, 2019

Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm.

Charbel Sakr, Naresh R. Shanbhag

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks.

Charbel Sakr, Naresh R. Shanbhag

Proceedings of the 2018 IEEE International Conference on Acoustics, 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

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Minimum Precision Requirements for Deep Learning with Biomedical Datasets.

Charbel Sakr, Naresh R. Shanbhag

Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference, 2018

2017

PredictiveNet: An energy-efficient convolutional neural network via zero prediction.

Yingyan Lin, Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag

Proceedings of the IEEE International Symposium on Circuits and Systems, 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, 2017

Minimum precision requirements for the SVM-SGD learning algorithm.

Charbel Sakr, Ameya D. Patil, Sai Zhang, Yongjune Kim, Naresh R. Shanbhag

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

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

2000

Carrier-Sense Protocols for Packet-Switched Smart Antenna Basestations.

Charbel Sakr, Terence D. Todd

Int. J. Wirel. Inf. Networks, 2000