Accelerate Intermittent Deep Inference
Fuente:
arXiv
Saved in:
| Main Author: | Zhang, Ziliang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Energy-Aware Decentralized Learning with Intermittent Model Training
by: Dhasade, Akash, et al.
Published: (2024)
by: Dhasade, Akash, et al.
Published: (2024)
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference
by: Zhang, Zeyu, et al.
Published: (2025)
by: Zhang, Zeyu, et al.
Published: (2025)
GPU-Accelerated Optimization of Transformer-Based Neural Networks for Real-Time Inference
by: Mukherjee, Soutrik, et al.
Published: (2026)
by: Mukherjee, Soutrik, et al.
Published: (2026)
Quasar: Quantized Self-Speculative Acceleration for Rapid Inference via Memory-Efficient Verification
by: Huang, Guang, et al.
Published: (2026)
by: Huang, Guang, et al.
Published: (2026)
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)
by: Luo, Shuqing, et al.
Published: (2025)
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
by: Feng, Hao, et al.
Published: (2024)
by: Feng, Hao, et al.
Published: (2024)
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology
by: Wu, Minghong, et al.
Published: (2025)
by: Wu, Minghong, et al.
Published: (2025)
Collaborative Speculative Inference for Efficient LLM Inference Serving
by: Gao, Luyao, et al.
Published: (2025)
by: Gao, Luyao, et al.
Published: (2025)
Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
by: Li, Zhuojin, et al.
Published: (2025)
by: Li, Zhuojin, et al.
Published: (2025)
FDC: Fast KV Dimensionality Compression for Efficient LLM Inference
by: Zhang, Zeyu, et al.
Published: (2024)
by: Zhang, Zeyu, et al.
Published: (2024)
PecSched: Preemptive and Efficient Cluster Scheduling for LLM Inference
by: Zhang, Zeyu, et al.
Published: (2024)
by: Zhang, Zeyu, et al.
Published: (2024)
Learning the Optimal Path and DNN Partition for Collaborative Edge Inference
by: Huang, Yin, et al.
Published: (2024)
by: Huang, Yin, et al.
Published: (2024)
Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference
by: Zhang, Haolin, et al.
Published: (2025)
by: Zhang, Haolin, et al.
Published: (2025)
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
by: Prabhakar, Rohan Baskar, et al.
Published: (2024)
by: Prabhakar, Rohan Baskar, et al.
Published: (2024)
Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
by: Wang, Tianze, et al.
Published: (2025)
by: Wang, Tianze, et al.
Published: (2025)
Making MoE-based LLM Inference Resilient with Tarragon
by: Zhang, Songyu, et al.
Published: (2026)
by: Zhang, Songyu, et al.
Published: (2026)
PackInfer: Compute- and I/O-Efficient Attention for Batched LLM Inference
by: Ning, Rui, et al.
Published: (2026)
by: Ning, Rui, et al.
Published: (2026)
Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing
by: Malekzadeh, Mohammad, et al.
Published: (2023)
by: Malekzadeh, Mohammad, et al.
Published: (2023)
Fast Distributed Inference Serving for Large Language Models
by: Wu, Bingyang, et al.
Published: (2023)
by: Wu, Bingyang, et al.
Published: (2023)
SERFLOW: A Cross-Service Cost Optimization Framework for SLO-Aware Dynamic ML Inference
by: Zhang, Zongshun, et al.
Published: (2025)
by: Zhang, Zongshun, et al.
Published: (2025)
Inference economics of language models
by: Erdil, Ege
Published: (2025)
by: Erdil, Ege
Published: (2025)
PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation
by: Butler, Branden, et al.
Published: (2024)
by: Butler, Branden, et al.
Published: (2024)
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
by: Zhang, Yujie, et al.
Published: (2024)
by: Zhang, Yujie, et al.
Published: (2024)
Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
by: Rajbhandari, Samyam, et al.
Published: (2025)
by: Rajbhandari, Samyam, et al.
Published: (2025)
Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching
by: Peng, Jie, et al.
Published: (2024)
by: Peng, Jie, et al.
Published: (2024)
FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
by: Hu, Zhengding, et al.
Published: (2026)
by: Hu, Zhengding, et al.
Published: (2026)
Split CNN Inference on Networked Microcontrollers
by: Lu, Junyu, et al.
Published: (2026)
by: Lu, Junyu, et al.
Published: (2026)
SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification
by: Miao, Xupeng, et al.
Published: (2023)
by: Miao, Xupeng, et al.
Published: (2023)
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
by: Lin, Wenxiang, et al.
Published: (2025)
by: Lin, Wenxiang, et al.
Published: (2025)
No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha
by: Agrawal, Amey, et al.
Published: (2024)
by: Agrawal, Amey, et al.
Published: (2024)
Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024)
by: Wang, Yuanli, et al.
Published: (2024)
HetCCL: Accelerating LLM Training with Heterogeneous GPUs
by: Kim, Heehoon, et al.
Published: (2026)
by: Kim, Heehoon, et al.
Published: (2026)
Pie: Pooling CPU Memory for LLM Inference
by: Xu, Yi, et al.
Published: (2024)
by: Xu, Yi, et al.
Published: (2024)
Practical Performance Guarantees for Pipelined DNN Inference
by: Archer, Aaron, et al.
Published: (2023)
by: Archer, Aaron, et al.
Published: (2023)
STAR: Decode-Phase Rescheduling for LLM Inference
by: Wang, Zhibin, et al.
Published: (2025)
by: Wang, Zhibin, et al.
Published: (2025)
Accelerating MoE Model Inference with Expert Sharding
by: Balmau, Oana, et al.
Published: (2025)
by: Balmau, Oana, et al.
Published: (2025)
Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices
by: Daghero, Francesco, et al.
Published: (2024)
by: Daghero, Francesco, et al.
Published: (2024)
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients
by: Luo, Ping, et al.
Published: (2024)
by: Luo, Ping, et al.
Published: (2024)
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
by: Akinwande, Victor, et al.
Published: (2024)
by: Akinwande, Victor, et al.
Published: (2024)
History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
by: He, Jingkai, et al.
Published: (2025)
by: He, Jingkai, et al.
Published: (2025)
Similar Items
-
Energy-Aware Decentralized Learning with Intermittent Model Training
by: Dhasade, Akash, et al.
Published: (2024) -
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference
by: Zhang, Zeyu, et al.
Published: (2025) -
GPU-Accelerated Optimization of Transformer-Based Neural Networks for Real-Time Inference
by: Mukherjee, Soutrik, et al.
Published: (2026) -
Quasar: Quantized Self-Speculative Acceleration for Rapid Inference via Memory-Efficient Verification
by: Huang, Guang, et al.
Published: (2026) -
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)