An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like machine learning (ML) and deep neural networks are becoming a de-facto standard. Nowa...

Full description

Bibliographic Details
Main Authors: Andrea Baroni, Artem Glukhov, Eduardo Pérez, Christian Wenger, Enrico Calore, Sebastiano Fabio Schifano, Piero Olivo, Daniele Ielmini, Cristian Zambelli
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Neuroscience
Subjects:
resistive RAM (RRAM)
drift
in-memory computing (IMC)
survival analysis
multi level conductance
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2022.932270/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2022.932270/full

Similar Items