Asynchronous Spike

Asynchronous spike processing leverages the event-driven nature of neural signals to create energy-efficient and computationally advantageous artificial intelligence systems. Current research focuses on developing and training spiking neural networks (SNNs) for various applications, including power grid management, computer vision (using architectures like RN-Net), and autonomous driving, often employing biologically-inspired models and training methods like surrogate gradient learning. This approach offers significant potential for improving the efficiency and performance of AI in resource-constrained environments, particularly in embedded systems and robotics.