Abstract
Tickysim is a clock tick-based simulator for the inter-chip interconnection network of the SpiNNaker architecture. Network devices such as arbiters, routers, and packet generators store, read, and write forward data through fixed-length FIFO buffers. At each clock tick, every component executes a “read” phase followed by a “write” phase. The structures of any finite graph which represents numerical quantities are known as topological indices. In this paper, we compute degree-based topological indices of the Tickysim SpiNNaker Model ( T S M ) sheet.
Highlights
Neural networks are applicable in many solutions for classification, prediction, control, etc.The variety of purposes is growing but with each new application the expectations are higher
At the highest level of abstraction, inter-chip architectures are basically mathematical graphs where each device is considered as vertex and the topology used between these devices reflects the edges and in total nature of graph
We consider the finite Tickysim SpiNNaker Model sheet which is obtained by hexagonal torus
Summary
Neural networks are applicable in many solutions for classification, prediction, control, etc. The variety of purposes is growing but with each new application the expectations are higher. We want neural networks to be more precise independently of the input data. Efficiency of the processing in a large manner depends on the training algorithm. This procedure is based on the random selection of weights in which neurons connections are burdened. During training process we implement a method which involves modification of the weights to minimize the response error of the entire structure see details in [1,2]
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