3 citations · 3 across the 8 of their papers we have counts for
8 papers
ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching
Xiaotian Qiu, Lukai Chen, Jinhao Li +3
Flow Matching (FM) policies have emerged as an efficient backbone for robotic control, offering fast and expressive action generation that underpins recent large-scale embodied AI…
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…
BalanceGS: Algorithm-System Co-design for Efficient 3D Gaussian Splatting Training on GPU
Junyi Wu, Jiaming Xu, Jinhao Li +4
3D Gaussian Splatting (3DGS) has emerged as a promising 3D reconstruction technique. The traditional 3DGS training pipeline follows three sequential steps: Gaussian densification,…
SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting
Jiaming Xu, Jiayi Pan, Yongkang Zhou +5
Early exiting has recently emerged as a promising technique for accelerating large language models (LLMs) by effectively reducing the hardware computation and memory access. In thi…
SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on Associative Processors
Mariam Rakka, Jinhao Li, Guohao Dai +3
Recent research efforts focus on reducing the computational and memory overheads of Large Language Models (LLMs) to make them feasible on resource-constrained devices. Despite adva…
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
Jinhao Li, Jiaming Xu, Shan Huang +9
Large Language Models (LLMs) have demonstrated remarkable capabilities across various fields, from natural language understanding to text generation. Compared to non-generative LLM…
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…
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
Jinhao Li, Jiaming Xu, Shiyao Li +4
Large language models (LLMs) have demonstrated impressive abilities in various domains while the inference cost is expensive. Many previous studies exploit quantization methods to…