2 citations · 4 across the 15 of their papers we have counts for
11 papers · 1 filter
ESR-HGNN: Eliminating Semantic Redundancy for Efficient Mini-batch HGNN Inference
Dengke Han, Mingyu Yan, Duo Wang +3
Heterogeneous graph neural networks (HGNNs) are highly effective in processing heterogeneous graph data and have been widely adopted in critical domains. As real-world graph data c…
AHASD: Asynchronous Heterogeneous Architecture for LLM Adaptive Drafting Speculative Decoding on Mobile Devices
Ma Zirui, Fan Zhihua, Li Wenxing +4
Speculative decoding enhances the inference efficiency of large language models (LLMs) by generating drafts using a small draft language model (DLM) and verifying them in batches w…
A Systematic Characterization of LLM Inference on GPUs
Haonan Wang, Xuxin Xiao, Mingyu Yan +8
This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four…
StreamDCIM: A Tile-based Streaming Digital CIM Accelerator with Mixed-stationary Cross-forwarding Dataflow for Multimodal Transformer
Shantian Qin, Ziqing Qiang, Zhihua Fan +4
Multimodal Transformers are emerging artificial intelligence (AI) models designed to process a mixture of signals from diverse modalities. Digital computing-in-memory (CIM) archite…
Multilayer Dataflow: Orchestrate Butterfly Sparsity to Accelerate Attention Computation
Haibin Wu, Wenming Li, Kai Yan +9
Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory de…
Accelerating Mini-batch HGNN Training by Reducing CUDA Kernels
Meng Wu, Jingkai Qiu, Mingyu Yan +5
Heterogeneous graph neural networks (HGNNs) are essential for capturing the structure and semantic information in heterogeneous graphs. However, existing GPU-based solutions, such…
Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective
Meng Wu, Mingyu Yan, Wenming Li +3
Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed sys…
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan +8
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both st…
Characterizing and Understanding HGNNs on GPUs
Mingyu Yan, Mo Zou, Xiaocheng Yang +4
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefo…
Multi-node Acceleration for Large-scale GCNs
Gongjian Sun, Mingyu Yan, Duo Wang +5
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount…
RISC-NN: Use RISC, NOT CISC as Neural Network Hardware Infrastructure
Taoran Xiang, Lunkai Zhang, Shuqian An +9
Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN app…