Imagen de Portada

Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ahmed, Soyed Tuhin, Hefenbrock, Michael, Tahoori, Mehdi B.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Minimizing CGYRO HPC Communication Costs in Ensembles with XGYRO by Sharing the Collisional Constant Tensor Structure
por: Sfiligoi, Igor, et al.
Publicado: (2025)

Accelerated Digital Twin Learning for Edge AI: A Comparison of FPGA and Mobile GPU
por: Xu, Bin, et al.
Publicado: (2025)

Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
por: Gong, Taesik, et al.
Publicado: (2023)

On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
por: Singh, Jaskirat, et al.
Publicado: (2024)

Ensemble Method for System Failure Detection Using Large-Scale Telemetry Data
por: Mudgal, Priyanka, et al.
Publicado: (2024)

Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection
por: Pasandideh, Faezeh, et al.
Publicado: (2026)

An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge
por: Boulougaris, Georgios, et al.
Publicado: (2024)

Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models
por: K., Prashanthi S., et al.
Publicado: (2025)

Quality Scalable Quantization Methodology for Deep Learning on Edge
por: Khaliq, Salman Abdul, et al.
Publicado: (2024)

E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
por: Zhang, Le, et al.
Publicado: (2024)

Portable, heterogeneous ensemble workflows at scale using libEnsemble
por: Hudson, Stephen, et al.
Publicado: (2024)

TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
por: Liu, Dong, et al.
Publicado: (2025)

Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural Networks Using One Bayesian Test Vector
por: Ahmed, Soyed Tuhin, et al.
Publicado: (2024)

Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
por: Pfeiffer, Kilian, 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)

Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum
por: Li, Lanpei, et al.
Publicado: (2025)

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
por: Jin, Jianli, et al.
Publicado: (2025)

LogAct: Enabling Agentic Reliability via Shared Logs
por: Balakrishnan, Mahesh, et al.
Publicado: (2026)

Pico-Cloud: Cloud Infrastructure for Tiny Edge Devices
por: Guri, Mordechai
Publicado: (2025)

An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence
por: Liu, Yichen, et al.
Publicado: (2026)

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
por: Arya, Mayank, et al.
Publicado: (2025)

Raft Distributed System for Multi-access Edge Computing Sharing Resources
por: Khaliq, Zain, et al.
Publicado: (2024)

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
por: Pinnock, Alyssa, et al.
Publicado: (2025)

Keep Your Friends Close: Leveraging Affinity Groups to Accelerate AI Inference Workflows
por: Garrett, Thiago, et al.
Publicado: (2023)

GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
por: Navardi, Mozhgan, et al.
Publicado: (2025)

AgileLog: A Forkable Shared Log for Agents on Data Streams
por: Bhat, Shreesha G., et al.
Publicado: (2026)

Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
por: Liang, Wenyi, et al.
Publicado: (2024)

Data Sharing at the Edge of the Network: A Disturbance Resilient Multi-modal ITS
por: Mikolasek, Igor, et al.
Publicado: (2024)

Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators
por: K., Prashanthi S., et al.
Publicado: (2023)

Fulcrum: Optimizing Concurrent DNN Training and Inferencing on Edge Accelerators
por: K., Prashanthi S., et al.
Publicado: (2025)

Mobile Edge Computing
por: Ahmed, Sohaib, et al.
Publicado: (2024)

Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions
por: Agarwal, Siddharth, et al.
Publicado: (2024)

Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion
por: Li, Yanchen, 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)

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors
por: Los, Denis, et al.
Publicado: (2025)

Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
por: Radchenko, Gleb, et al.
Publicado: (2024)

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
por: Luo, Yizhou, et al.
Publicado: (2024)

HDEE: Heterogeneous Domain Expert Ensemble
por: Ersoy, Oğuzhan, et al.
Publicado: (2025)

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
por: Asif, Abdullah Al, et al.
Publicado: (2026)

Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
por: Mohsin, Muhammad Ahmed, et al.
Publicado: (2025)