24 citations · 28 across the 4 of their papers we have counts for
5 papers
SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference
Shanghao Liu, Renze Chen, Size Zheng +4
Video diffusion transformers (vDiTs) generate high quality but pay quadratic self-attention cost, making inference prohibitive at video-token scales. The challenge is input-adaptiv…
DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
Zihao Zheng, Xiuping Cui, Size Zheng +4
As the Mix-of-Experts (MoE) architecture increases the number of parameters in large models, there is an even greater need for model quantization. However, existing quantization me…
vMCU: Coordinated Memory Management and Kernel Optimization for DNN Inference on MCUs
Size Zheng, Renze Chen, Meng Li +3
IoT devices based on microcontroller units (MCU) provide ultra-low power consumption and ubiquitous computation for near-sensor deep learning models (DNN). However, the memory of M…
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
Yilong Zhao, Chien-Yu Lin, Kan Zhu +7
The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM s…
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Qingcheng Xiao, Size Zheng, Bingzhe Wu +3
Tensor computations overwhelm traditional general-purpose computing devices due to the large amounts of data and operations of the computations. They call for a holistic solution c…