Asynchronous Architecture

Asynchronous architecture focuses on designing and implementing systems where components operate and communicate independently, without strict synchronization, aiming for improved efficiency and responsiveness. Current research emphasizes asynchronous approaches in diverse areas, including spiking neural networks (SNNs), multi-agent reinforcement learning (MARL), and large language models, often employing novel training methods like unlayered backpropagation or asynchronous credit assignment frameworks and leveraging architectures such as transformers and recurrent convolutional neural networks. This research is significant because asynchronous designs offer potential advantages in speed, energy efficiency, and scalability across various applications, from neuromorphic computing and edge AI to distributed optimization and large-scale machine learning.