Name
Title | ||
|---|---|---|
A fully flexible circuit implementation of clique-based neural networks in 65-nm CMOS. |
Abstract | ||
|---|---|---|
Clique-based neural networks implement low-complexity functions working with a reduced connectivity between neurons. Thus, they address very specific applications operating with a very low-energy budget. However, the implementation in the state of the art is not flexible and a fabricated circuit is only usable in a unique use case. Besides, the silicon area of hardwired circuits grows exponentiall... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSI.2018.2881508 | IEEE Transactions on Circuits and Systems I: Regular Papers |
Keywords | Field | DocType |
Neurons,Synapses,Biological neural networks,Silicon,Leakage currents,Complexity theory | Computer architecture,Low energy,Clique,Computer science,Electronic engineering,CMOS,Cmos asic,Artificial neural network | Conference |
Volume | Issue | ISSN |
66 | 5 | 1549-8328 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benoit Larras | 1 | 12 | 4.66 |
| Paul Chollet | 2 | 1 | 1.75 |
| Cyril Lahuec | 3 | 29 | 9.17 |
| Fabrice Seguin | 4 | 36 | 16.02 |
| Matthieu Arzel | 5 | 69 | 15.10 |