Reduced complexity algorithms for cognitive packet network routers
Computer Communications
A new multi-constrained QoS routing algorithm in mobile ad hoc networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
A new qos routing optimal algorithm in mobile ad hoc networks based on hopfield neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Modelling and analysis of gene regulatory networks based on the G-network
International Journal of Advanced Intelligence Paradigms
Hi-index | 0.00 |
Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n2 to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.