Improvement of Spiking Neural Network with Bit Planes and Color Models
This work addresses a performance bottleneck in SNNs for image processing, offering an incremental improvement that could enhance efficiency in computational neuroscience and AI applications.
The paper tackled performance optimization in spiking neural networks (SNNs) for images by introducing a novel coding method using bit plane representation, achieving improved accuracy without increasing model size across multiple datasets.
Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications.