Name
Abstract | ||
|---|---|---|
Artificial neural network chips can achieve high-speed performance in solving complex computational problems for signal and information processing applications. These chips contain regular circuit units such as synapse matrices that interconnect linear arrays of input and output neurons. The neurons and synapses may be implemented in an analog or digital design style. Although the neural processing has some degree of fault tolerance, a significant percentage of processing defects can result in catastrophic failure of the neural network processors. Systematic testing of these arrays of circuitry is of great importance in order to assure the quality and reliability of VLSI neural network processor chips. The proposed testing method consists of parametric test and behavioral test. Two programmable analog neural chips have been designed and fabricated. The systematic approach used to test the chips is described, and measurement results on parametric test are presented. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1007/BF02106451 | VLSI Signal Processing |
Keywords | Field | DocType |
Output Neuron,Input Neuron,Current Mirror,Synapse Cell,Transfer Curve | Current mirror,Computer science,Real-time computing,Input/output,Catastrophic failure,Parametric statistics,Fault tolerance,Artificial neural network,Computer hardware,Interconnection,Very-large-scale integration,Embedded system | Journal |
Volume | Issue | ISSN |
8 | 3 | 0922-5773 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sudhir M. Gowda | 1 | 19 | 4.73 |
| Bing J. Sheu | 2 | 115 | 21.86 |
| Wen-Jay Hsu | 3 | 31 | 3.53 |