Name
Title | ||
|---|---|---|
Deviation-tolerant floating gate structures as a way to design an on-chip learning neural networks |
Abstract | ||
|---|---|---|
Hardware implementation of artificial neural networks (ANN) based on MOS transistors with floating gate (Neuron MOS or νMOS)
is discussed. Choosing analog approach as a weight storage rather than digital improves learning accuracy, minimizes chip
area and power dissipation. However, since weight value can be represented by any voltage in the range of supplied voltage
(e.g. from 0 to 3.3 V), minimum difference of two values is very small, especially in the case of using neuron with large
sum of weights. This implies that ANN using analog hardware approach is weak against V
dd
deviation. The purpose of this paper is to investigate main parts of analog ANN circuits (synapse and neuron) that can compensate
all kinds of deviation and to develop their design methodologies. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/s00500-001-0162-6 | Soft Comput. |
Keywords | Field | DocType |
design methodology,neural network,artificial neural network,chip,power dissipation | Mathematical optimization,Computer science,Dissipation,Voltage,Electronic engineering,Chip,Artificial intelligence,Transistor,Artificial neural network,Electronic circuit | Journal |
Volume | Issue | Citations |
6 | 6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafail Lashevsky | 1 | 0 | 0.68 |
| Yohey Sato | 2 | 0 | 0.34 |