VLM Fine-tuning on Memory-Constrained Edge Devices: Forward-Only Optimization for On-Device Learning
Автор: CosmoX
Загружено: 2026-01-15
Просмотров: 1
📌 Based on Amazon Science research, this video explains how to fine-tune Vision-Language Models (VLMs) under strict memory constraints on edge / on-device environments.
🧠 Key takeaways
🧩 Why standard VLM fine-tuning fails on memory-limited edge devices
🧮 Backpropagation vs. forward-only passes: what changes on-device
🛠️ A hybrid optimization approach for memory-constrained fine-tuning
📈 Reported accuracy gains compared to existing BP-free techniques
🧪 Practical trade-offs: compute, memory, and deployment constraints
🚀 Implications for edge AI personalization and multimodal applications
#VLM #VisionLanguageModel #FineTuning #EdgeAI #OnDeviceLearning #ForwardOnly #MemoryEfficient #MultimodalAI #AmazonScience #EfficientAI
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