Asynchronous Execution

Asynchronous execution, a paradigm where tasks or computations proceed without strict synchronization, is gaining traction across diverse fields, aiming to improve efficiency and scalability. Current research focuses on optimizing asynchronous algorithms in federated learning, large language model interaction, and spiking neural networks, often incorporating techniques like quantization, sparsification, and novel training methods to mitigate challenges arising from the lack of synchronization. These advancements hold significant promise for accelerating model training, enhancing real-time responsiveness in applications like event-based vision and high-frequency trading, and improving resource utilization in high-performance computing.