Title
Computational memory-based inference and training of deep neural networks
Abstract
In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.
Year
DOI
Venue
2019
10.23919/VLSIT.2019.8776518
2019 Symposium on VLSI Technology
Keywords
Field
DocType
In-memory computing,deep learning,PCM
Inference,Explosive material,In-Memory Processing,Electronic engineering,Artificial intelligence,Deep learning,Engineering,Deep neural networks
Conference
ISSN
ISBN
Citations 
0743-1562
978-1-7281-1530-6
0
PageRank 
References 
Authors
0.34
0
16