Spike Based Neuromorphic

Spike-based neuromorphic computing aims to build energy-efficient artificial intelligence systems by mimicking the brain's spiking neural networks (SNNs). Current research focuses on improving SNN training methods, including developing more efficient spike encoding techniques and algorithms like consistent ANN-SNN conversion and backpropagation adapted for spike-based hardware. These advancements address limitations in training and inference speed, leading to significant memory and energy savings compared to traditional artificial neural networks, with applications in signal processing, computer vision, and other resource-constrained environments. The ultimate goal is to create highly efficient and powerful neuromorphic hardware for various AI applications.