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ORCIDCharbel Sakr
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2020 – today
- 2024
[i10]Charbel Sakr, Brucek Khailany:
ESPACE: Dimensionality Reduction of Activations for Model Compression. CoRR abs/2410.05437 (2024)- [i9]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:
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation. CoRR abs/2410.21271 (2024) - 2023
- [j6]Ben Keller
, Rangharajan Venkatesan, Steve Dai, Stephen G. Tell, Brian Zimmer, Charbel Sakr, William J. Dally, C. Thomas Gray, Brucek Khailany:
A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm. IEEE J. Solid State Circuits 58(4): 1129-1141 (2023) - [c14]Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam, Jason Yik, Thierry Tambe, Charbel Sakr, Stephen W. Keckler, Vijay Janapa Reddi:
VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning. IROS 2023: 6304-6309 - [i8]Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam, Jason Yik, Thierry Tambe, Charbel Sakr, Stephen W. Keckler, Vijay Janapa Reddi:
VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning. CoRR abs/2310.07854 (2023) - 2022
- [j5]Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(10): 3188-3201 (2022) - [c13]Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, William J. Dally, Brucek Khailany:
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training. ICML 2022: 19123-19138 - [i7]Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, William J. Dally, Brucek Khailany:
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training. CoRR abs/2206.06501 (2022) - 2021
- [b1]Charbel Sakr:
Finite precision deep learning with theoretical guarantees. University of Illinois Urbana-Champaign, USA, 2021 - [j4]Hassan Dbouk, Sujan K. Gonugondla, Charbel Sakr, Naresh R. Shanbhag:
A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting. IEEE J. Solid State Circuits 56(7): 2234-2244 (2021) - [j3]Charbel Sakr, Naresh R. Shanbhag:
Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures. IEEE Trans. Signal Process. 69: 6462-6472 (2021) - [c12]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:
Optimizing Selective Protection for CNN Resilience. ISSRE 2021: 127-138 - 2020
- [c11]Hassan Dbouk, Sujan K. Gonugondla, Charbel Sakr, Naresh R. Shanbhag:
KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting. CICC 2020: 1-4 - [c10]Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on the Precision of In-memory Architectures. ICCAD 2020: 128:1-128:9 - [i6]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:
HarDNN: Feature Map Vulnerability Evaluation in CNNs. CoRR abs/2002.09786 (2020) - [i5]Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications. CoRR abs/2012.13645 (2020)
2010 – 2019
- 2019
- [j2]Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag:
Minimum Precision Requirements of General Margin Hyperplane Classifiers. IEEE J. Emerg. Sel. Topics Circuits Syst. 9(2): 253-266 (2019) - [c9]Charbel Sakr, Naresh R. Shanbhag:
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. ICLR (Poster) 2019 - [c8]Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh R. Shanbhag, Kailash Gopalakrishnan:
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. ICLR (Poster) 2019 - [i4]Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh R. Shanbhag, Kailash Gopalakrishnan:
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. CoRR abs/1901.06588 (2019) - 2018
- [c7]Charbel Sakr, Naresh R. Shanbhag:
Minimum Precision Requirements for Deep Learning with Biomedical Datasets. BioCAS 2018: 1-4 - [c6]Charbel Sakr, Naresh R. Shanbhag:
An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks. ICASSP 2018: 1090-1094 - [c5]Charbel Sakr, Jungwook Choi, Zhuo Wang, Kailash Gopalakrishnan, Naresh R. Shanbhag:
True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization. ICASSP 2018: 2346-2350 - [i3]Charbel Sakr, Naresh R. Shanbhag:
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. CoRR abs/1812.11732 (2018) - 2017
- [c4]Charbel Sakr, Ameya D. Patil, Sai Zhang, Yongjune Kim, Naresh R. Shanbhag:
Minimum precision requirements for the SVM-SGD learning algorithm. ICASSP 2017: 1138-1142 - [c3]Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag:
Analytical Guarantees on Numerical Precision of Deep Neural Networks. ICML 2017: 3007-3016 - [c2]Yingyan Lin, Charbel Sakr, Yongjune Kim, Naresh R. Shanbhag:
PredictiveNet: An energy-efficient convolutional neural network via zero prediction. ISCAS 2017: 1-4 - 2016
- [i2]Sai Zhang, Mingu Kang, Charbel Sakr, Naresh R. Shanbhag:
Reducing the Energy Cost of Inference via In-sensor Information Processing. CoRR abs/1607.00667 (2016) - [i1]Charbel Sakr, Ameya Patil, Sai Zhang, Naresh R. Shanbhag:
Understanding the Energy and Precision Requirements for Online Learning. CoRR abs/1607.00669 (2016)
2000 – 2009
- 2000
- [j1]Charbel Sakr, Terence D. Todd:
Carrier-Sense Protocols for Packet-Switched Smart Antenna Basestations. Int. J. Wirel. Inf. Networks 7(3): 133-148 (2000)
1990 – 1999
- 1997
- [c1]Charbel Sakr, Terence D. Todd:
Carrier-sense protocols for packet-switched smart antenna basestations. ICNP 1997: 45-52
Coauthor Index
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