Inference Acceleration

Inference acceleration focuses on optimizing the speed and efficiency of running large-scale machine learning models, such as large language models (LLMs), vision-language models (LVLMs), and graph neural networks (GNNs), without sacrificing accuracy. Current research emphasizes techniques like model pruning, efficient attention mechanisms, parallelization strategies (including CPU-based acceleration and distributed inference), and novel decoding methods (e.g., multi-head decoding and lookahead strategies). These advancements are crucial for deploying these computationally intensive models in resource-constrained environments and for improving the responsiveness and scalability of AI applications across various domains.