Name
Abstract | ||
|---|---|---|
There is a pressing need for energy-efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. We have developed an approach that efficiently performs a wide range of machine learning tasks such as compressed sensing, unsupervised learning, solving systems of linear equations, and deep learning. |
Year | Venue | Field |
|---|---|---|
2018 | ERCIM NEWS | Efficient energy use,Computer science,In-Memory Processing,Artificial intelligence |
DocType | Volume | Issue |
Journal | 2018 | 115 |
ISSN | Citations | PageRank |
0926-4981 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Le Gallo | 1 | 47 | 9.73 |
| Sebastian, A. | 2 | 267 | 44.35 |
| Evangelos Eleftheriou | 3 | 1590 | 118.20 |