5 accepted papers
Large language models (LLMs) face significant deployment challenges due to their massive computational demands. While pruning offers a promising compression solution, existing methods suffer from two critical limitations: (1) They neglect activation distribution shifts between calibration data and t
The demand for long-context processing in large language models (LLMs) continues to escalate alongside rapid advancements in their capabilities. However, the intermediate attention keys and values (KV cache) employed to avoid re-computations, also grow linearly with sequence length, far exceeding th
LiDAR-inertial odometry(LIO) has been widely applied in intelligent robotics and autonomous driving, providing high-precision and low-latency ego-motion estimation. However, the massive point clouds generated by LiDAR introduce intensive data processing demands, making k-nearest neighbor(KNN) search
LiDAR-inertial odometry(LIO) has been widely applied in intelligent robotics and autonomous driving, providing high-precision and low-latency ego-motion estimation. However, the massive point clouds generated by LiDAR introduce intensive data processing demands, making k-nearest neighbor(KNN) search…
With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to r…