Structure-based fitness prediction for the variable-structure DANNA neuromorphic architecture

In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), where thousands of networks are generated and then evaluated by determining fitness scores on specific tasks. Fitness scores guide the generation of new networks until a target fitness is achieved. One source of performance overhead during EO is the simulation of the task on each network to determing its fitness. To mitigate this source of overhead, we formulate the Static Fitness Prediction Task (SFPT), for predicting a network's fitness without direct simulation. Our hypothesis is that we can use SFPT to predict a network's fitness sufficiently accurately to reject a significant portion of networks during EO without having to simulate them, thereby making the EO more efficient. We propose a data-driven approach to the SFPT on the neuromorphic model DANNA [1]. Our approach transforms networks into directed graphs and extracts structural features to train an ancillary model for predicting the fitness of new networks. We analyze the extracted features and evaluate several predictive models to predict the fitness of networks for five tasks. Our results demonstrate a predictive capacity in these features and models. Our primary contribution is to demonstrate the utility of graph-level features extracted from variable-structure networks to predict network fitness and circumvent expensive simulations.