Embedded Arena: Iterative Optimization via Hardware Feedback
Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference due to latency, communication, or privacy constraints. Optimizing models for heterogeneous microcontrollers (MCUs) requires simultaneously satisfying hard physical constraints on memory, power, and temperature while preserving accuracy, a multidimensional optimization that is today performed manually by experts. We ask whether an LLM agent can autonomously navigate this complex, multi-turn pipeline guided by real hardware feedback, and introduce a hardware-in-the-loop agent arena in which the agent iteratively refines both model and firmware -- compiling, flashing, and measuring on real har...
| Authors | Zhihan Zhang, Alexander Le Metzger, Jiuyang Lyu et al. |
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| Primary category | cs.AR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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