Place and Route Algorithms for a Neuromorphic Communication Network Simulator

Abstract: In recent years, neural networks have seen increased interest from both the cognitive computing and computation neuroscience fields. Neuromorphic computing systems simulate neural network efficiently, but have not yet reached the amount of neurons that a mammal has. Increasing this quantity is an aspiration, but more neurons will also increase the traffic load of the system. The placement of the neurons onto the neuromorphic computing system has a significant effect on the network load. This thesis introduces algorithms for placing a large amount of neurons in an efficient and agile way. First, an analysis of placement algorithms for very large scale integration design is done, displaying that computing complexity of these algorithms is high. When using the predefined underlying structure of the neural network, more rapid algorithms can be used. The results show that the population placement algorithm has high computing speed as well as providing exceptional result.