3 citations · 3 across the 7 of their papers we have counts for
7 papers
DynSplit-KV: Dynamic Semantic Splitting for KVCache Compression in Efficient Long-Context LLM Inference
Jiancai Ye, Jun Liu, Qingchen Li +5
Although Key-Value (KV) Cache is essential for efficient large language models (LLMs) inference, its growing memory footprint in long-context scenarios poses a significant bottlene…
STEP: Warm-Started Visuomotor Policies with Spatiotemporal Consistency Prediction
Jinhao Li, Yuxuan Cong, Yingqiao Wang +5
Diffusion policies have recently emerged as a powerful paradigm for visuomotor control in robotic manipulation due to their ability to model the distribution of action sequences an…
Speculative Decoding for Verilog: Speed and Quality, All in One
Changran Xu, Yi Liu, Yunhao Zhou +3
The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programm…
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Ziyang Zheng, Shan Huang, Jianyuan Zhong +4
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT…
MARCA: Mamba Accelerator with ReConfigurable Architecture
Jinhao Li, Shan Huang, Jiaming Xu +4
We propose a Mamba accelerator with reconfigurable architecture, MARCA.We propose three novel approaches in this paper. (1) Reduction alternative PE array architecture for both lin…
SARO: Space-Aware Robot System for Terrain Crossing via Vision-Language Model
Shaoting Zhu, Derun Li, Linzhan Mou +3
The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks. However, there are few explorations for these foundation models used in…
Large Trajectory Models are Scalable Motion Predictors and Planners
Qiao Sun, Shiduo Zhang, Danjiao Ma +7
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding div…