shilpakancharla/snn-networking

Neuromorphic computing is a new technology which uses very-large-scale integration (VLSI) systems containing electrical analogs that mimic neurobiological structures. Such technology can be simulated using the Python framework snnTorch, a derivative of the deep learning framework PyTorch that has the capacity to run spiking neural networks (SNNs) which in turn can be run on neuromorphic chips. This project serves to explore the use of the snnTorch framework on a networking dataset from the Barcelona Neural Networking Center. Not only are the performance results and construction of a spiking neural network seen from this experiment, but also important characteristics of types of data that can be collected that will better fit the spatio-temporal data characteristics that are ideal to be run in an SNN. Moreover, cloud computing tools from Amazon Web Service (AWS) such as S3 and SageMaker were used to aid in the completion of running deep learning experiments. This work serves as an artificial intelligence proof of concept for the snnTorch framework and cloud computing in the deep learning environment.

Python