Network Bottleneck

Network bottlenecks, limitations in data transfer speed or processing capacity within a system, hinder the performance of various applications, from large language models to federated learning and machine learning accelerators. Current research focuses on mitigating these bottlenecks through techniques like data compression (e.g., optimizing key-value cache usage in LLMs), algorithmic improvements (e.g., stochastic parameter updates in federated learning), and architectural changes (e.g., employing silicon photonic interposers in hardware accelerators). Overcoming these limitations is crucial for improving the efficiency, scalability, and performance of numerous computational systems and applications across diverse fields.