Name
Abstract | ||
|---|---|---|
Online learning for data analysis, categorization and anomaly detection has become a key technique in a range of adaptive embedded applications. In this article the authors propose a very low power encoder design using a sparse, hyperdimensional representation of letters and words in natural language and they show that this representation and their design can be used with high energy efficiency fo... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MDAT.2017.2740839 | IEEE Design & Test |
Keywords | Field | DocType |
High definition video,Encoding,Switches,Associative memory,Computers,System-on-chip,Power demand,Electronic learning | Categorization,Anomaly detection,System on a chip,Content-addressable memory,Computer science,Speech recognition,Software system,Natural language,Encoder,Encoding (memory) | Journal |
Volume | Issue | ISSN |
34 | 6 | 2168-2356 |
Citations | PageRank | References |
7 | 0.52 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohsen Imani | 1 | 341 | 48.13 |
| John Hwang | 2 | 7 | 0.52 |
| Tajana Simunic | 3 | 3198 | 266.23 |
| Abbas Rahimi | 4 | 467 | 35.26 |
| Jan M. Rabaey | 5 | 4796 | 1049.96 |