The Evaluation of Current Spiking Neural Network Conversion Methods in Radar Data

Download or read book The Evaluation of Current Spiking Neural Network Conversion Methods in Radar Data written by Colton C. Smith and published by . This book was released on 2021 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: The continued growth and application of deep learning has resulted in a vast increase in energy and computational requirements. Biologically inspired spiking neural networks (SNNs) and neuromorphic hardware pose one possible solution to this issue. Optimization of these methods, however, remains difficult and less effective compared with that of traditional artificial neural networks (ANNs). A number of methods have been recently proposed to optimize SNNs through the conversion of architecturally equivalent ANNs. However, most benchmarking of these methods has only been done separately through experiments in the respective papers. Therefore, the performance of the solutions is inevitably biased due to the differences in levels and goals of optimization. Moreover, certain papers also relied heavily on architectural improvements to the base ANN which can be separated from the actual method of conversion [1] [2]. In this thesis, we thoroughly evaluate and compare the performance of the major ANN-to SNN conversion solutions based on a new set of performance metrics we proposed. Additionally, we implement expansions to certain methods, allowing for more comprehensive and fair comparisons. Furthermore, the hyperparameters of each method are optimized uniformly to reduce biases towards specific methods. Our implementations and comparisons of SNN solutions are carried out on one-dimensional radar data. To the best of our knowledge, this is the first such effort in the domain of radar applications.