Artikel dengan mandat akses publik - Charbel SakrPelajari lebih lanjut
Tersedia di suatu tempat: 13
Analytical guarantees on numerical precision of deep neural networks
C Sakr, Y Kim, N Shanbhag
International Conference on Machine Learning, 3007-3016, 2017
Mandat: US Department of Defense
PredictiveNet: An energy-efficient convolutional neural network via zero prediction
Y Lin, C Sakr, Y Kim, N Shanbhag
2017 IEEE international symposium on circuits and systems (ISCAS), 1-4, 2017
Mandat: US Department of Defense
An analytical method to determine minimum per-layer precision of deep neural networks
C Sakr, N Shanbhag
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
Mandat: US Department of Defense
Optimizing Selective Protection for CNN Resilience.
A Mahmoud, SKS Hari, CW Fletcher, SV Adve, C Sakr, NR Shanbhag, ...
ISSRE, 127-138, 2021
Mandat: US National Science Foundation, US Department of Defense
Fundamental limits on the precision of in-memory architectures
SK Gonugondla, C Sakr, H Dbouk, NR Shanbhag
Proceedings of the 39th International Conference on Computer-Aided Design, 1-9, 2020
Mandat: US Department of Defense
True gradient-based training of deep binary activated neural networks via continuous binarization
C Sakr, J Choi, Z Wang, K Gopalakrishnan, N Shanbhag
2018 IEEE international conference on acoustics, speech and signal …, 2018
Mandat: US Department of Defense
A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting
H Dbouk, SK Gonugondla, C Sakr, NR Shanbhag
IEEE Journal of Solid-State Circuits 56 (7), 2234-2244, 2020
Mandat: US Department of Energy, US Department of Defense
Minimum precision requirements for the SVM-SGD learning algorithm
C Sakr, A Patil, S Zhang, Y Kim, N Shanbhag
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
Mandat: US Department of Defense
Signal processing methods to enhance the energy efficiency of in-memory computing architectures
C Sakr, NR Shanbhag
IEEE Transactions on Signal Processing 69, 6462-6472, 2021
Mandat: US Department of Defense
Fundamental limits on energy-delay-accuracy of in-memory architectures in inference applications
SK Gonugondla, C Sakr, H Dbouk, NR Shanbhag
IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2021
Mandat: US Department of Defense
KeyRAM: A 0.34 uJ/decision 18 k decisions/s recurrent attention in-memory processor for keyword spotting
H Dbouk, SK Gonugondla, C Sakr, NR Shanbhag
2020 IEEE Custom Integrated Circuits Conference (CICC), 1-4, 2020
Mandat: US Department of Defense
Minimum precision requirements for deep learning with biomedical datasets
C Sakr, N Shanbhag
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 1-4, 2018
Mandat: US Department of Defense
Minimum Precision Requirements of General Margin Hyperplane Classifiers
C Sakr, Y Kim, N Shanbhag
IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9 (2 …, 2019
Mandat: US Department of Defense
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