Imagen de Portada

MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xue, Runzhen, Wu, Hao, Yan, Mingyu, Xiao, Ziheng, Ye, Xiaochun, Fan, Dongrui
Formato:	Preprint
Publicado:	2025
Materias:	Hardware Architecture Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

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

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

Accelerating GNN Training through Locality-aware Dropout and Merge
por: Sun, Gongjian, et al.
Publicado: (2025)

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

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

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

OneDSE: A Unified Microprocessor Metric Prediction and Design Space Exploration Framework
por: Raj, Ritik, et al.
Publicado: (2025)

SoberDSE: Sample-Efficient Design Space Exploration via Learning-Based Algorithm Selection
por: Xu, Lei, et al.
Publicado: (2026)

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

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
por: Li, Runkai, et al.
Publicado: (2025)

FIFOAdvisor: A DSE Framework for Automated FIFO Sizing of High-Level Synthesis Designs
por: Abi-Karam, Stefan, et al.
Publicado: (2025)

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

Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models
por: Xu, Lei, et al.
Publicado: (2025)

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

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

MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Exploration
por: Xu, Lei, et al.
Publicado: (2026)

Cool-3D: An End-to-End Thermal-Aware Framework for Early-Phase Design Space Exploration of Microfluidic-Cooled 3DICs
por: Wang, Runxi, et al.
Publicado: (2025)

DiffuSE: Cross-Layer Design Space Exploration of DNN Accelerator via Diffusion-Driven Optimization
por: Ren, Yi, et al.
Publicado: (2025)

Voyager: An End-to-End Framework for Design-Space Exploration and Generation of DNN Accelerators
por: Prabhu, Kartik, et al.
Publicado: (2025)

LLM-DSE: Searching Accelerator Parameters with LLM Agents
por: Wang, Hanyu, et al.
Publicado: (2025)

Anatomy of the gem5 Simulator: AtomicSimpleCPU, TimingSimpleCPU, O3CPU, and Their Interaction with the Ruby Memory System
por: Söderström, Johan, et al.
Publicado: (2025)

QiMeng-CPU-v2: Automated Superscalar Processor Design by Learning Data Dependencies
por: Cheng, Shuyao, et al.
Publicado: (2025)

Processing-in-memory for genomics workloads
por: Simon, William Andrew, et al.
Publicado: (2025)

MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration
por: Que, Zhiqiang, et al.
Publicado: (2025)

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

SPEC CPU2026: Characterization, Representativeness, and Cross-Suite Comparison
por: Li, Ruihao, et al.
Publicado: (2026)

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators
por: Sakhuja, Chirag, et al.
Publicado: (2024)

Efficient Task Transfer for HLS DSE
por: Ding, Zijian, et al.
Publicado: (2024)

Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators
por: Symons, Arne, et al.
Publicado: (2022)

RapidChiplet: A Toolchain for Rapid Design Space Exploration of Chiplet Architectures
por: Iff, Patrick, et al.
Publicado: (2023)

ACALSim: A Scalable Parallel Simulation Framework for High-Performance System Design Space Exploration
por: Lin, Wei-Fen, et al.
Publicado: (2026)

gem5 Co-Pilot: AI Assistant Agent for Architectural Design Space Exploration
por: Fu, Zuoming, et al.
Publicado: (2025)

DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration
por: Ghosh, Arkapravo, et al.
Publicado: (2025)

EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge
por: Bai, Kangbo, et al.
Publicado: (2025)

HOPE: Holistic STT-RAM Architecture Exploration Framework for Future Cross-Platform Analysis
por: SeyedFaraji, Saeed, et al.
Publicado: (2024)

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

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

ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput
por: Kim, Junsoo, et al.
Publicado: (2025)

Kugelblitz: Executable, Cost-Aware Design-Space Exploration for Programmable Packet Pipelines
por: Ageev, Artem, et al.
Publicado: (2023)