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Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

M. Rasch, C. Mackin, M. Gallo, A. Chen, A. Fasoli, F. Odermatt, N. Li, S. Nandakumar, P. Narayanan, H. Tsai, G. Burr, A. Sebastian, and V. Narayanan. CoRR, (2023)

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S. R. Keshava

Ramya Nandakumar

Kannabiran Nandakumar

Murti von R S Devarakonda

Teresa S L R A Terreri

Other publications of authors with the same name

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.S. Nandakumar, I. Boybat, M. Gallo, E. Eleftheriou, A. Sebastian, and B. Rajendran. CoRR, (2019)Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.M. Rasch, C. Mackin, M. Gallo, A. Chen, A. Fasoli, F. Odermatt, N. Li, S. Nandakumar, P. Narayanan, H. Tsai and 3 other author(s). CoRR, (2023)Computational memory-based inference and training of deep neural networks.A. Sebastian, I. Boybat, M. Dazzi, I. Giannopoulos, V. Jonnalagadda, V. Joshi, G. Karunaratne, B. Kersting, R. Khaddam-Aljameh, S. Nandakumar and 6 other author(s). VLSI Circuits, page 168-. IEEE, (2019)Live demonstration: Spiking neural circuit based navigation inspired by C. elegans thermotaxis.C. Shetty, S. Nitchith, R. Rawat, S. Nandakumar, P. Shah, S. Kulkarni, and B. Rajendran. ISCAS, page 1905. IEEE, (2015)Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation.R. Bruce, S. Sarwat, I. Boybat, C. Cheng, W. Kim, S. Nandakumar, C. Mackin, T. Philip, Z. Liu, K. Brew and 21 other author(s). IRPS, page 1-6. IEEE, (2021)An efficient synaptic architecture for artificial neural networks.I. Boybat, M. Gallo, S. Nandakumar, T. Moraitis, T. Tuma, B. Rajendran, Y. Leblebici, A. Sebastian, and E. Eleftheriou. NVMTS, page 1-4. IEEE, (2017)Deep learning acceleration based on in-memory computing.E. Eleftheriou, M. Gallo, S. Nandakumar, C. Piveteau, I. Boybat, V. Joshi, R. Khaddam-Aljameh, M. Dazzi, I. Giannopoulos, G. Karunaratne and 7 other author(s). IBM J. Res. Dev., 63 (6): 7:1-7:16 (2019)HERMES Core - A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing.R. Khaddam-Aljameh, M. Stanisavljevic, J. Mas, G. Karunaratne, M. Braendli, F. Liu, A. Singh, S. Müller, U. Egger, A. Petropoulos and 14 other author(s). VLSI Circuits, page 1-2. IEEE, (2021)Phase-Change Memory Models for Deep Learning Training and Inference.S. Nandakumar, I. Boybat, V. Joshi, C. Piveteau, M. Gallo, B. Rajendran, A. Sebastian, and E. Eleftheriou. ICECS, page 727-730. IEEE, (2019)Impact of conductance drift on multi-PCM synaptic architectures.I. Boybat, S. Nandakumar, M. Gallo, B. Rajendran, Y. Leblebici, A. Sebastian, and E. Eleftheriou. NVMTS, page 1-4. IEEE, (2018)

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Disambiguation of "Nandakumar, S. R."

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Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

Please choose a person to relate this publication to

S. R. Keshava

Ramya Nandakumar

Kannabiran Nandakumar

Murti von R S Devarakonda

Teresa S L R A Terreri

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nandakumar, S. R."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

Please choose a person to relate this publication to

S. R. Keshava

Ramya Nandakumar

Kannabiran Nandakumar

Murti von R S Devarakonda

Teresa S L R A Terreri

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.