NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning explores NEST optimizes distributed deep learning training by intelligently placing model components across devices, considering network topology, memory constraints, and parallelism strategies.. Commercial viability score: 7/10 in LLM Training.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisMONET: Modeling and Optimization of neural NEtwork Training from Edge to Data CentersViability: 3.0
- Builds On ThisNode Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AIViability: 3.0
- Builds On ThisSpatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid ForecastingViability: 6.0
- Builds On ThisNESTOR: A Nested MOE-based Neural Operator for Large-Scale PDE Pre-TrainingViability: 5.0
- Builds On ThisResource-Efficient Iterative LLM-Based NAS with Feedback Memory