This survey paper proposes a unified framework for deploying foundation model powered agents across edge-cloud systems while optimizing resources and ensuring reliable service delivery.
-----
https://arxiv.org/abs/2412.13437
🤔 Original Problem:
→ Deploying foundation model (FM) powered agents faces major challenges with fluctuating query loads, massive parameter spaces, and diverse service requirements
→ Edge devices have limited storage and compute capacity, making it difficult to run large models efficiently
→ Current systems struggle with real-time processing and multi-agent collaboration
-----
🔧 Solution in this Paper:
→ A multi-layer optimization framework integrates execution, resource, model and application layers
→ The execution layer focuses on low-level optimizations across FPGAs, ASICs, CPUs and GPUs
→ Resource layer implements parallelism and dynamic scaling based on workload
→ Model layer applies compression and token reduction for efficient deployment
→ Agent layer manages memory, planning, and multi-agent coordination
-----
💡 Key Insights:
→ Collaborative edge-cloud inference improves system efficiency through dynamic task allocation
→ Model compression and parallelism are crucial for edge deployment
→ Resource scaling must adapt to varying query loads and service requirements
→ Multi-agent frameworks need efficient memory management and coordination mechanisms
Share this post