Name
Abstract | ||
|---|---|---|
A new architecture and a statistical model for a pulse-mode digital multilayer neural network (DMNN) are presented. Algebraic neural operations are replaced by stochastic processes using pseudo-random pulse sequences. Synaptic weights and neuron states are represented as probabilities and estimated as average rates of pulse occurrences in corresponding pulse sequences. A statistical model of error (or noise) is developed to estimate relative accuracy associated with stochastic computing in terms of mean and variance. The stochastic computing technique is implemented with simple logic gates as basic computing elements leading to a high neuron-density on a chip. Furthermore, the use of simple logic gates for neural operations, the pulse-mode signal representation, and the modular design techniques lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Any size of a feedforward network can be configured where processing speed is independent of the network size. Multilayer feedforward networks are modeled and applied to pattern classification problems such as encoding and character recognition. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/72.410355 | IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council |
Keywords | Field | DocType |
multilayer feedforward network,pulse-mode digital multilayer neural,network size,flexible network architecture,simple logic gate,statistical model,neural operation,corresponding pulse sequence,algebraic neural operation,feedforward network,basic computing element,encoding,modular design,architecture,stochastic process,neural networks,logic gates,statistical analysis,stochastic resonance,stochastic processes,chip,logic design,logic gate,network architecture,probability,vlsi | Logic synthesis,Computer science,Stochastic neural network,Network architecture,Probabilistic neural network,Time delay neural network,Statistical model,Artificial intelligence,Artificial neural network,Stochastic computing,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 5 | 1045-9227 |
Citations | PageRank | References |
22 | 3.42 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngchul Kim | 1 | 92 | 21.26 |
| Michael A. Shanblatt | 2 | 155 | 52.25 |