Neuromorphic Technology

Neuromorphic technology aims to build computing systems inspired by the brain's architecture and function, prioritizing energy efficiency and high processing speed. Current research focuses on developing and training spiking neural networks (SNNs), often employing novel training algorithms like pseudo-zeroth-order methods to overcome limitations of traditional backpropagation. Applications span diverse fields, including speech recognition, emotion recognition from neuromorphic sensor data, and robotics through sensor fusion techniques like lidar-event camera calibration. The ultimate goal is to create hardware and software that mimic biological neural networks for improved performance and reduced energy consumption in various applications.