Cover Image

Accelerating GNN Training through Locality-aware Dropout and Merge

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sun, Gongjian, Yan, Mingyu, Han, Dengke, Xue, Runzhen, Wang, Duo, Ye, Xiaochun, Fan, Dongrui
Format:	Preprint
Published:	2025
Subjects:	Hardware Architecture
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ADE-HGNN: Accelerating HGNNs through Attention Disparity Exploitation
by: Han, Dengke, et al.
Published: (2024)

SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
by: Xue, Runzhen, et al.
Published: (2024)

GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling
by: Xue, Runzhen, et al.
Published: (2024)

HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
by: Xue, Runzhen, et al.
Published: (2023)

TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
by: Han, Dengke, et al.
Published: (2025)

Characterizing and Understanding HGNN Training on GPUs
by: Han, Dengke, et al.
Published: (2024)

MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration
by: Xue, Runzhen, et al.
Published: (2025)

Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
by: Xue, Runzhen, et al.
Published: (2024)

Accelerating Mini-batch HGNN Training by Reducing CUDA Kernels
by: Wu, Meng, et al.
Published: (2024)

Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective
by: Wu, Meng, et al.
Published: (2024)

A Systematic Characterization of LLM Inference on GPUs
by: Wang, Haonan, et al.
Published: (2025)

StreamDCIM: A Tile-based Streaming Digital CIM Accelerator with Mixed-stationary Cross-forwarding Dataflow for Multimodal Transformer
by: Qin, Shantian, et al.
Published: (2025)

Multilayer Dataflow: Orchestrate Butterfly Sparsity to Accelerate Attention Computation
by: Wu, Haibin, et al.
Published: (2024)

SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations
by: Wang, Zhican, et al.
Published: (2025)

ApproxPilot: A GNN-based Accelerator Approximation Framework
by: Zhang, Qing, et al.
Published: (2024)

Be CIM or Be Memory: A Dual-mode-aware DNN Compiler for CIM Accelerators
by: Zhao, Shixin, et al.
Published: (2025)

DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System
by: Luo, Yi, et al.
Published: (2025)

Scope: A Scalable Merged Pipeline Framework for Multi-Chip-Module NN Accelerators
by: Huang, Zongle, et al.
Published: (2026)

AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance
by: Kang, Seungkwan, et al.
Published: (2026)

L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
by: Gao, Yiming, et al.
Published: (2026)

Low Latency GNN Accelerator for Quantum Error Correction
by: Cicero, Alessio, et al.
Published: (2026)

ApproxGNN: A Pretrained GNN for Parameter Prediction in Design Space Exploration for Approximate Computing
by: Vlcek, Ondrej, et al.
Published: (2025)

PIMSYN: Synthesizing Processing-in-memory CNN Accelerators
by: Li, Wanqian, et al.
Published: (2024)

PIMSIM-NN: An ISA-based Simulation Framework for Processing-in-Memory Accelerators
by: Wang, Xinyu, et al.
Published: (2024)

Guac: Energy-Aware and SSA-Based Generation of Coarse-Grained Merged Accelerators from LLVM-IR
by: Brumar, Iulian, et al.
Published: (2024)

PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators
by: Sun, Xiaotian, et al.
Published: (2024)

RAS: A Bit-Exact rANS Accelerator For High-Performance Neural Lossless Compression
by: Qin, Yuchao, et al.
Published: (2025)

CoroAMU: Unleashing Memory-Driven Coroutines through Latency-Aware Decoupled Operations
by: Jiang, Zhuolun, et al.
Published: (2025)

HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator
by: Ogbogu, Chukwufumnanya, et al.
Published: (2025)

Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
by: Lin, Chenqi, et al.
Published: (2025)

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling
by: Zhong, Ruizhe, et al.
Published: (2024)

SoMa: Identifying, Exploring, and Understanding the DRAM Communication Scheduling Space for DNN Accelerators
by: Cai, Jingwei, et al.
Published: (2025)

Large Processor Chip Model
by: Chang, Kaiyan, et al.
Published: (2025)

VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator
by: Wang, Zhican, et al.
Published: (2025)

Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA
by: Zhang, Zehuan, et al.
Published: (2024)

StreamTensor: Make Tensors Stream in Dataflow Accelerators for LLMs
by: Ye, Hanchen, et al.
Published: (2025)

A Tensor-Train Decomposition based Compression of LLMs on Group Vector Systolic Accelerator
by: Huang, Sixiao, et al.
Published: (2025)

AssertGen: Enhancement of LLM-aided Assertion Generation through Cross-Layer Signal Bridging
by: Lyu, Hongqin, et al.
Published: (2025)

Generalized Ping-Pong: Off-Chip Memory Bandwidth Centric Pipelining Strategy for Processing-In-Memory Accelerators
by: Wang, Ruibao, et al.
Published: (2024)

FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training
by: Lu, Jinming, et al.
Published: (2025)