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Showing 1–9 of 9 results

Advanced filters: Author: Vijay Narayanan Clear advanced filters

Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip

The authors report the implementation of a Transformer-based model on the same architecture used in Large Language Models in a 14nm analog AI accelerator with 35 million Phase Change Memory devices, which achieves near iso-accuracy despite hardware imperfections and noise.

An Chen
Stefano Ambrogio
Geoffrey W. Burr
ResearchOpen Access30 Sept 2025
Nature Communications

Volume: 16, P: 1-11
Genetically restricted suppressor T-cell clones derived from lepromatous leprosy lesions

Robert L. Modlin
Hideyuki Kato
Barry R. Bloom
Research31 Jul 1986
Nature

Volume: 322, P: 459-461
Erratum: Switching of ferroelectric polarization in epitaxial BaTiO₃ films on silicon without a conducting bottom electrode

Catherine Dubourdieu
John Bruley
Vijay Narayanan
Amendments and Corrections07 Nov 2013
Nature Nanotechnology

Volume: 8, P: 881
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing promises efficient DNN inference acceleration but suffers from nonidealities. Here, hardware-aware training methods are improved so that various larger DNNs of diverse topologies nevertheless achieve iso-accuracy.

Malte J. Rasch
Charles Mackin
Vijay Narayanan
ResearchOpen Access30 Aug 2023
Nature Communications

Volume: 14, P: 1-18
Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing

This study shows a viable pathway to the efficient deployment of state-of-the-art large language models using mixture of experts on 3D analog in-memory computing hardware.

Julian Büchel
Athanasios Vasilopoulos
Abu Sebastian
Research08 Jan 2025
Nature Computational Science

Volume: 5, P: 13-26
Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning

Petru Manescu
Priya Narayanan
Delmiro Fernandez-Reyes
ResearchOpen Access13 Feb 2023
Scientific Reports

Volume: 13, P: 1-11
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference

A multicore analogue in-memory computing chip that is designed and fabricated in 14 nm complementary metal–oxide–semiconductor technology with backend-integrated phase-change memory can be used for deep neural network inference.

Manuel Le Gallo
Riduan Khaddam-Aljameh
Abu Sebastian
Research10 Aug 2023
Nature Electronics

Volume: 6, P: 680-693
Switching of ferroelectric polarization in epitaxial BaTiO₃ films on silicon without a conducting bottom electrode

Reversible switching of ferroelectric polarization can now be achieved in oxides grown epitaxially on silicon.

Catherine Dubourdieu
John Bruley
Vijay Narayanan
Research29 Sept 2013
Nature Nanotechnology

Volume: 8, P: 748-754
Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array

Edward J Saunders
Tokhir Dadaev
Zsofia Kote-Jarai
ResearchOpen Access10 Mar 2016
British Journal of Cancer

Volume: 114, P: 945-952