Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

S R Nandakumar

Showing results (1-10 of 7) with videos related to

Pageof 1
Sort By:
Nano Letters|February 6, 2016
A 250 mV Cu/SiO2/W Memristor with Half-Integer Quantum Conductance StatesS R Nandakumar, Marie Minvielle, Saurabh Nagar, et al.
Scientific Reports|May 17, 2020
Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory SynapsesS R Nandakumar, Irem Boybat, Manuel Le Gallo, et al.
Nature Communications|June 30, 2018
Neuromorphic computing with multi-memristive synapsesIrem Boybat, Manuel Le Gallo, S R Nandakumar, et al.
Nature Communications|May 20, 2020
Accurate deep neural network inference using computational phase-change memoryVinay Joshi, Manuel Le Gallo, Simon Haefeli, et al.
Nature Communications|August 30, 2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based acceleratorsMalte J Rasch, Charles Mackin, Manuel Le Gallo, et al.
Frontiers in Neuroscience|June 2, 2020
Mixed-Precision Deep Learning Based on Computational MemoryS R Nandakumar, Manuel Le Gallo, Christophe Piveteau, et al.
Nature Communications|June 30, 2022
Optimised weight programming for analogue memory-based deep neural networksCharles Mackin, Malte J Rasch, An Chen, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Nano Letters|February 6, 2016
A 250 mV Cu/SiO2/W Memristor with Half-Integer Quantum Conductance StatesS R Nandakumar, Marie Minvielle, Saurabh Nagar, et al.
Scientific Reports|May 17, 2020
Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory SynapsesS R Nandakumar, Irem Boybat, Manuel Le Gallo, et al.
Nature Communications|June 30, 2018
Neuromorphic computing with multi-memristive synapsesIrem Boybat, Manuel Le Gallo, S R Nandakumar, et al.
Nature Communications|May 20, 2020
Accurate deep neural network inference using computational phase-change memoryVinay Joshi, Manuel Le Gallo, Simon Haefeli, et al.
Nature Communications|August 30, 2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based acceleratorsMalte J Rasch, Charles Mackin, Manuel Le Gallo, et al.
Frontiers in Neuroscience|June 2, 2020
Mixed-Precision Deep Learning Based on Computational MemoryS R Nandakumar, Manuel Le Gallo, Christophe Piveteau, et al.
Nature Communications|June 30, 2022
Optimised weight programming for analogue memory-based deep neural networksCharles Mackin, Malte J Rasch, An Chen, et al.
Pageof 1