3 citations · 3 across the 8 of their papers we have counts for
9 papers
EagleVLA: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
Zebin Yang, Qi Wang, Yunhe Wang +6
Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks. However, deploying VLA models on low-power onboard devices, such as the Jetson O…
KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning
Zebin Yang, Tong Xie, Baotong Lu +3
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and envir…
DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation
Zebin Yang, Yijiahao Qi, Tong Xie +3
Vision-Language-Action (VLA) models have shown remarkable success in robotic tasks like manipulation by fusing a language model's reasoning with a vision model's 3D understanding.…
EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval
Zebin Yang, Sunjian Zheng, Tong Xie +6
Object-goal navigation (ObjNav) tasks an agent with navigating to the location of a specific object in an unseen environment. Embodied agents equipped with large language models (L…
LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design
Renjie Wei, Songqiang Xu, Linfeng Zhong +5
State space models (SSMs) like Mamba have recently attracted much attention. Compared to Transformer-based large language models (LLMs), Mamba achieves linear computation complexit…
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers
Zebin Yang, Renze Chen, Taiqiang Wu +5
In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding…
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Chenqi Lin, Tianshi Xu, Zebin Yang +3
With the fast evolution of large language models (LLMs), privacy concerns with user queries arise as they may contain sensitive information. Private inference based on homomorphic…
ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
Shuzhang Zhong, Zebin Yang, Meng Li +3
Recent advancements in generative large language models (LLMs) have significantly boosted the performance in natural language processing tasks. However, their efficiency is hampere…
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology
Rongqing Cong, Wenyang He, Mingxuan Li +5
Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented…