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"EmbedGenius: Towards Automated Software Development for Generic Embedded IoT Systems"

The podcast on this paper is generated with Google's Illuminate.

EmbedGenius automates embedded IoT system development by combining LLMs with hardware expertise, eliminating manual intervention and reducing development complexity.

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https://arxiv.org/abs/2412.09058

🤖 Original Problem:

→ Embedded IoT development requires extensive cross-domain knowledge of hardware and software

→ Manual development is time-consuming, error-prone, and demands significant expertise in handling diverse hardware modules and dependencies

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🔧 Solution in this Paper:

→ EmbedGenius introduces component-aware library resolution to handle hardware dependencies automatically

→ It implements library knowledge generation to inject domain expertise into LLMs

→ The system uses auto-programming with nested reasoning loops for compile and flash verification

→ Memory-augmented LLMs generate task prompts and automate system programming

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💡 Key Insights:

→ Automated library selection significantly improves coding accuracy by 10.8%

→ Selective memory pick-up reduces token consumption by 26.2%

→ Auto-programming with feedback loops ensures 95.7% coding accuracy

→ The system works across 71 hardware modules and 4 embedded platforms

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📊 Results:

→ 95.7% coding accuracy across 350+ IoT tasks

→ 86.5% task completion success rate

→ Outperforms baselines by 15.6%-37.7% in coding accuracy

→ Reduces development time to 2.6-3.1 minutes for complex systems

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