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Phase-change memory models for deep learning training and inference
S. R. Nandakumar
, Irem Boybat
, Vinay Joshi
, Christophe Piveteau
, Manuel Le Gallo
, Bipin Rajendran
, Abu Sebastian
, Evangelos Eleftheriou
Electrical and Computer Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
24
Scopus citations
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Keyphrases
Phase Change Random Access Memory (PCRAM)
100%
Memory Models
100%
Deep Learning Inference
100%
Deep Learning Training
100%
Memory Device
50%
In-memory Computing
50%
Training Performance
50%
Hardware Implementation
50%
Inference Performance
50%
Temporal Evolution
25%
Memory-based
25%
Simple Device
25%
Conductance
25%
Hardware Efficiency
25%
Non-volatile
25%
Deep Neural Network
25%
On chip
25%
Device Modeling
25%
Array-based
25%
Accurate Model
25%
Inference Engine
25%
Non-ideality
25%
State Dependence
25%
Analog Memory
25%
Read Noise
25%
Conductance States
25%
TensorFlow
25%
Deep Learning Framework
25%
Conductance Drift
25%
Connection Strength
25%
Memory Array
25%
Deep Learning Hardware
25%
Efficient Inference
25%
Connectivity Matrix
25%
Computer Science
Deep Learning
100%
Deep Neural Network
100%
Memory Model
100%
Hardware Implementation
66%
Learning Framework
33%
Nonvolatile
33%
Inference Engines
33%
Temporal Evolution
33%
Connection Strength
33%
Connectivity Matrix
33%
Memory Array
33%