Articles with public access mandates - Naresh ShanbhagLearn more
Not available anywhere: 8
A multi-functional in-memory inference processor using a standard 6T SRAM array
M Kang, SK Gonugondla, A Patil, NR Shanbhag
IEEE Journal of Solid-State Circuits 53 (2), 642-655, 2018
Mandates: US National Science Foundation, US Department of Defense
A variation-tolerant in-memory machine learning classifier via on-chip training
SK Gonugondla, M Kang, NR Shanbhag
IEEE Journal of Solid-State Circuits 53 (11), 3163-3173, 2018
Mandates: US Department of Defense
An in-memory VLSI architecture for convolutional neural networks
M Kang, S Lim, S Gonugondla, NR Shanbhag
IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (3 …, 2018
Mandates: US National Science Foundation, US Department of Defense
A 19.4-nJ/decision, 364-K decisions/s, in-memory random forest multi-class inference accelerator
M Kang, SK Gonugondla, S Lim, NR Shanbhag
IEEE Journal of Solid-State Circuits 53 (7), 2126-2135, 2018
Mandates: US National Science Foundation, US Department of Defense
Deep in-memory architectures in SRAM: An analog approach to approximate computing
M Kang, SK Gonugondla, NR Shanbhag
Proceedings of the IEEE 108 (12), 2251-2275, 2020
Mandates: US Department of Defense
Enhancing the Accuracy of Resistive In-Memory Architectures using Adaptive Signal Processing
HM Ou, NR Shanbhag
ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023
Mandates: US Department of Defense
A rank decomposed statistical error compensation technique for robust convolutional neural networks in the near threshold voltage regime
Y Lin, S Zhang, NR Shanbhag
Journal of Signal Processing Systems 90, 1439-1451, 2018
Mandates: US Department of Defense
High-Speed Data Transmission over Twisted-Pair Channels
NR Shanbhag
Digital Signal Processing for Multimedia Systems, 109-138, 2018
Mandates: US National Science Foundation
Available somewhere: 37
Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care
HU Chung, BH Kim, JY Lee, J Lee, Z Xie, EM Ibler, KH Lee, A Banks, ...
Science 363 (6430), eaau0780, 2019
Mandates: US National Science Foundation, Bill & Melinda Gates Foundation, US …
A 42pJ/decision 3.12 TOPS/W robust in-memory machine learning classifier with on-chip training
SK Gonugondla, M Kang, N Shanbhag
2018 IEEE International Solid-State Circuits Conference-(ISSCC), 490-492, 2018
Mandates: US Department of Defense
Analytical guarantees on numerical precision of deep neural networks
C Sakr, Y Kim, N Shanbhag
International Conference on Machine Learning, 3007-3016, 2017
Mandates: 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
Mandates: US Department of Defense
PROMISE: An end-to-end design of a programmable mixed-signal accelerator for machine-learning algorithms
P Srivastava, M Kang, SK Gonugondla, S Lim, J Choi, V Adve, NS Kim, ...
2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018
Mandates: US National Science Foundation, US Department of Defense
An MRAM-based deep in-memory architecture for deep neural networks
AD Patil, H Hua, S Gonugondla, M Kang, NR Shanbhag
2019 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2019
Mandates: 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
Mandates: US Department of Defense
A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array
M Kang, SK Gonugondla, NR Shanbhag
ESSCIRC 2017-43rd IEEE European Solid State Circuits Conference, 263-266, 2017
Mandates: 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
Mandates: 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
Mandates: US Department of Defense
Shannon-inspired statistical computing for the nanoscale era
NR Shanbhag, N Verma, Y Kim, AD Patil, LR Varshney
Proceedings of the IEEE 107 (1), 90-107, 2018
Mandates: US National Science Foundation, 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
Mandates: US Department of Defense
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