ANNSyS: An Analog Neural Network Synthesis System

Bayraktaroǧlu I., Öǧrenci A. S., DÜNDAR G., Balkir S., Alpaydin E.

Neural Networks, cilt.12, sa.2, ss.325-338, 1999 (Scopus) identifier identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 2
  • Basım Tarihi: 1999
  • Doi Numarası: 10.1016/s0893-6080(98)00122-1
  • Dergi Adı: Neural Networks
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.325-338
  • Anahtar Kelimeler: Circuit simulation, CMOS neural networks, Macromodeling, Madaline rule, Multilayer perceptrons, Neural network implementations, Neural network training
  • Boğaziçi Üniversitesi Adresli: Evet

Özet

A synthesis system based on a circuit simulator and a silicon assembler for analog neural networks to be implemented in MOS technology is presented. The system approximates on-chip training of the neural network under consideration and provides the best starting point for 'chip-in-the-loop training'. Behaviour of the analog neural network circuitry is modeled according to its SPICE simulations and those models are used in the initial training of the analog neural networks prior to the fine tuning stage. In this stage, the simulator has been combined with Madaline Rule III for approximating on chip training by software, thus minimizing the effects of circuit nonidealities on neural networks. The circuit simulator partitions the circuit into decoupled blocks which can be simulated separately, with the output of one block being the input for the next one. Finally, the silicon assembler generates the layout for the neural network by reading analog standard cells from a library. The system's performance has been demonstrated by several examples. A synthesis system based on circuit simulator and a silicon assembler for analog neural networks to be implemented in MOS technology presented. The system approximates on-chip training of the neural network under consideration and provides the best starting point for `chip-in-the-loop training'. Behaviour of the analog neural network circuitry is modeled according to its SPICE simulations and those models are used in the initial training of the analog neural networks prior to the fine tuning stage. In this stage, the simulator has been combined with Madaline Rule III for approximating on chip training by software, thus minimizing the effects of circuit nonidealities on neural networks. The circuit simulator partitions the circuit into decoupled blocks which can be simulated separately, with the output of one block being the input for the next one. Finally, the silicon assembler generates the layout for the neural network by reading analog standard cells from a library. The system's performance has been demonstrated by several examples.