Paper Info
Title
Phase-change memory enables energy-efficient brain-inspired computing
Abstract
As we embark on an era of artificial intelligence (AI) and cognitive computing, there is a pressing need for energy-efficient computing systems for highly data-centric AI related applications. It is becoming increasingly clear that to build efficient cognitive computers, we need to transition to novel architectures where memory and processing are better collocated. Computational memory is one such approach where the physical attributes and state dynamics of memory devices are exploited to perform certain computational tasks in place with very high areal and energy efficiency (Fig. 1) [1]. Phase-change memory (PCM) devices based on materials such as Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> are appealing for computational memory owing to their multi-level storage capability and potential scalability. In this paper, we present a brief overview of our work towards enabling energy-efficient brain-inspired computing using PCM.
Year
DOI
Venue
2019
10.1109/DRC46940.2019.9046459
2019 Device Research Conference (DRC)
Keywords
DocType
ISSN
multilevel storage capability,PCM devices,phase-change memory devices,energy efficiency,computational memory,cognitive computers,highly data-centric AI,energy-efficient computing systems,cognitive computing,artificial intelligence,energy-efficient brain-inspired computing,Ge2Sb2Te5
Conference
1548-3770
ISBN
Citations 
PageRank 
978-1-7281-2113-0
0
0.34
References 
Authors
2
3
Name
Order
Citations
PageRank
Manuel Le Gallo1479.73
Sebastian, A.226744.35
Evangelos Eleftheriou31590118.20