Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment
Fuente:
arXiv
Guardado en:
| Autores principales: | Qazi, Muhammad Azlan, Iosifidis, Alexandros, Zhang, Qi |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices
por: Wang, Li, et al.
Publicado: (2024)
por: Wang, Li, et al.
Publicado: (2024)
ECCENTRIC: Edge-Cloud Collaboration Framework for Distributed Inference Using Knowledge Adaptation
por: Kamani, Mohammad Mahdi, et al.
Publicado: (2025)
por: Kamani, Mohammad Mahdi, et al.
Publicado: (2025)
Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach
por: Bajpai, Divya Jyoti, et al.
Publicado: (2024)
por: Bajpai, Divya Jyoti, et al.
Publicado: (2024)
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
por: Wei, Jianyu, et al.
Publicado: (2024)
por: Wei, Jianyu, et al.
Publicado: (2024)
A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
por: Wu, Qi, et al.
Publicado: (2026)
por: Wu, Qi, et al.
Publicado: (2026)
On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
por: Singh, Jaskirat, et al.
Publicado: (2024)
por: Singh, Jaskirat, et al.
Publicado: (2024)
SparOA: Sparse and Operator-aware Hybrid Scheduling for Edge DNN Inference
por: Zhang, Ziyang, et al.
Publicado: (2025)
por: Zhang, Ziyang, et al.
Publicado: (2025)
EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
por: Pinnock, Alyssa, et al.
Publicado: (2025)
por: Pinnock, Alyssa, et al.
Publicado: (2025)
Large Language Model Partitioning for Low-Latency Inference at the Edge
por: Kafetzis, Dimitrios, et al.
Publicado: (2025)
por: Kafetzis, Dimitrios, et al.
Publicado: (2025)
A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs
por: Zhang, Chen, et al.
Publicado: (2026)
por: Zhang, Chen, et al.
Publicado: (2026)
Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation
por: Kim, Joon Ha, et al.
Publicado: (2026)
por: Kim, Joon Ha, et al.
Publicado: (2026)
EdgeReasoning: Characterizing Reasoning LLM Deployment on Edge GPUs
por: Kubwimana, Benjamin, et al.
Publicado: (2025)
por: Kubwimana, Benjamin, et al.
Publicado: (2025)
Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection
por: Pasandideh, Faezeh, et al.
Publicado: (2026)
por: Pasandideh, Faezeh, et al.
Publicado: (2026)
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum
por: Li, Lanpei, et al.
Publicado: (2025)
por: Li, Lanpei, et al.
Publicado: (2025)
Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
por: Jin, Jianli, et al.
Publicado: (2025)
por: Jin, Jianli, et al.
Publicado: (2025)
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
por: Zhou, Wenqing, et al.
Publicado: (2025)
por: Zhou, Wenqing, et al.
Publicado: (2025)
Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices
por: Li, Xiangyu, et al.
Publicado: (2025)
por: Li, Xiangyu, et al.
Publicado: (2025)
DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72
por: Li, Wanqian, et al.
Publicado: (2026)
por: Li, Wanqian, et al.
Publicado: (2026)
Demystifying the Communication Characteristics for Distributed Transformer Models
por: Anthony, Quentin, et al.
Publicado: (2024)
por: Anthony, Quentin, et al.
Publicado: (2024)
An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence
por: Liu, Yichen, et al.
Publicado: (2026)
por: Liu, Yichen, et al.
Publicado: (2026)
Reconstruction-Based Adaptive Scheduling Using AI Inferences in Safety-Critical Systems
por: Alshaer, Samer, et al.
Publicado: (2025)
por: Alshaer, Samer, et al.
Publicado: (2025)
xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
por: Fang, Jiarui, et al.
Publicado: (2024)
por: Fang, Jiarui, et al.
Publicado: (2024)
HiDP: Hierarchical DNN Partitioning for Distributed Inference on Heterogeneous Edge Platforms
por: Taufique, Zain, et al.
Publicado: (2024)
por: Taufique, Zain, et al.
Publicado: (2024)
Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach
por: Bajpai, Divya Jyoti, et al.
Publicado: (2024)
por: Bajpai, Divya Jyoti, et al.
Publicado: (2024)
FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference
por: Liu, Xing, et al.
Publicado: (2025)
por: Liu, Xing, et al.
Publicado: (2025)
Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
por: Wang, Zhanghan, et al.
Publicado: (2025)
por: Wang, Zhanghan, et al.
Publicado: (2025)
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
por: Li, Jun, et al.
Publicado: (2024)
por: Li, Jun, et al.
Publicado: (2024)
SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
por: Xie, Jincheng, et al.
Publicado: (2026)
por: Xie, Jincheng, et al.
Publicado: (2026)
CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment
por: Li, Qinfeng, et al.
Publicado: (2024)
por: Li, Qinfeng, et al.
Publicado: (2024)
Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks
por: Deng, Xiumei, et al.
Publicado: (2025)
por: Deng, Xiumei, et al.
Publicado: (2025)
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
por: Peng, Xian, et al.
Publicado: (2025)
por: Peng, Xian, et al.
Publicado: (2025)
MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
por: Yu, Dianhai, et al.
Publicado: (2022)
por: Yu, Dianhai, et al.
Publicado: (2022)
Deploying Foundation Model Powered Agent Services: A Survey
por: Xu, Wenchao, et al.
Publicado: (2024)
por: Xu, Wenchao, et al.
Publicado: (2024)
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
por: Mounesan, Motahare, et al.
Publicado: (2024)
por: Mounesan, Motahare, et al.
Publicado: (2024)
Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator
por: Peccia, Federico Nicolas, et al.
Publicado: (2024)
por: Peccia, Federico Nicolas, et al.
Publicado: (2024)
ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
por: Shen, Zixu, et al.
Publicado: (2025)
por: Shen, Zixu, et al.
Publicado: (2025)
Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions
por: Zhang, Zongshun, et al.
Publicado: (2025)
por: Zhang, Zongshun, et al.
Publicado: (2025)
Trust-Aware Routing for Distributed Generative AI Inference at the Edge
por: Nguyen, Chanh, et al.
Publicado: (2026)
por: Nguyen, Chanh, et al.
Publicado: (2026)
PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning
por: Wang, Yisu, et al.
Publicado: (2025)
por: Wang, Yisu, et al.
Publicado: (2025)
EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update
por: Li, Jiaxing, et al.
Publicado: (2024)
por: Li, Jiaxing, et al.
Publicado: (2024)
Ejemplares similares
-
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices
por: Wang, Li, et al.
Publicado: (2024) -
ECCENTRIC: Edge-Cloud Collaboration Framework for Distributed Inference Using Knowledge Adaptation
por: Kamani, Mohammad Mahdi, et al.
Publicado: (2025) -
Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach
por: Bajpai, Divya Jyoti, et al.
Publicado: (2024) -
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
por: Wei, Jianyu, et al.
Publicado: (2024) -
A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
por: Wu, Qi, et al.
Publicado: (2026)