Name
Abstract | ||
|---|---|---|
Encoded neural networks mix the principles of associative memories and error-correcting decoders. Their storage capacity has been shown to be much larger than Hopfield Neural Networks'. This paper introduces an analog implementation of this new type of network. The proposed circuit has been designed for the 1V supply ST CMOS 65nm process. It consumes 1165 times less energy than a digital equivalent circuit while being 2.7 times more efficient in terms of combined speed and surface. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISCAS.2013.6572170 | ISCAS |
Keywords | Field | DocType |
CMOS analogue integrated circuits,content-addressable storage,decoding,error correction codes,neural nets,Hopfield neural networks,ST CMOS process,analog implementation,associative memories,encoded neural networks,error-correcting decoders,size 65 nm,storage capacity,voltage 1 V | Digital electronics,Analogue electronics,Content-addressable memory,Computer science,CMOS,Electronic engineering,Content-addressable storage,Decoding methods,Artificial neural network,Equivalent circuit | Conference |
ISSN | Citations | PageRank |
0271-4302 | 3 | 0.45 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benoit Larras | 1 | 12 | 4.66 |
| Cyril Lahuec | 2 | 29 | 9.17 |
| Matthieu Arzel | 3 | 69 | 15.10 |
| Fabrice Seguin | 4 | 36 | 16.02 |