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arXiv · 2512.05073

David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?

Shashwat Shankar·Subhranshu Pandey·Innocent Dengkhw Mochahari·Bhabesh Mali·Animesh Basak Chowdhury·Sukanta Bhattacharjee·Chandan Karfa
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Abstract

Large Language Model(LLM) inference demands massive compute and energy, making domain-specific tasks expensive and unsustainable. As foundation models keep scaling, we ask: Is bigger always better for hardware design? Our work tests this by evaluating Small Language Models coupled with a curated agentic AI framework on NVIDIA's Comprehensive Verilog Design Problems(CVDP) benchmark. Results show that agentic workflows: through task decomposition, iterative feedback, and correction - not only unlock near-LLM performance at a fraction of the cost but also create learning opportunities for agents, paving the way for efficient, adaptive solutions in complex design tasks.

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Shashwat Shankar, Subhranshu Pandey, Innocent Dengkhw Mochahari, Bhabesh Mali, Animesh Basak Chowdhury, Sukanta Bhattacharjee, Chandan Karfa. 2025-12-04. David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?. https://arxiv.org/abs/2512.05073

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