Name
Abstract | ||
|---|---|---|
Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multicore reduced-instruction set computing processor. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MC.2016.312 | Computer |
Keywords | Field | DocType |
Memristors,Neurons,Multicore processing,Switches,Random access memory,Energy efficiency,Neuromemristive systems,Neural networks,Power system management,Low power electronics,Energy efficiency | Power management,Software engineering,Supercomputer,Efficient energy use,Computer security,Computer science,Boosting (machine learning),Artificial neural network,Multi-core processor,Von Neumann architecture,Low-power electronics | Journal |
Volume | Issue | ISSN |
49 | 10 | 0018-9162 |
Citations | PageRank | References |
5 | 0.52 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cory E. Merkel | 1 | 32 | 6.51 |
| Raqibul Hasan | 2 | 76 | 8.74 |
| Nicholas Soures | 3 | 9 | 1.94 |
| Dhireesha Kudithipudi | 4 | 93 | 27.31 |
| Tarek M. Taha | 5 | 280 | 32.89 |
| Sapan Agarwal | 6 | 13 | 4.07 |
| Matthew J. Marinella | 7 | 25 | 7.43 |