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

Source: arXiv - 2512.05073v1

Overview

The paper investigates whether tiny language models—when paired with an “agentic” AI workflow—can rival the performance of massive LLMs on a demanding hardware‑design benchmark. By coupling small models with a structured loop of task decomposition, feedback, and correction, the authors achieve near‑state‑of‑the‑art results on NVIDIA’s Comprehensive Verilog Design Problems (CVDP) while using a fraction of the compute and energy budget.

Key Contributions

• Agentic AI framework for small models – a reusable pipeline that adds task‑level reasoning, iterative self‑correction, and external tool integration to otherwise modest LLMs.
• Empirical evaluation on CVDP – the first systematic comparison of tiny (≤ 1 B parameters) versus large (≥ 10 B) models for end‑to‑end hardware design tasks.
• Cost‑performance trade‑off analysis – quantifies compute, latency, and energy savings (up to 80 % reduction) while preserving design‑quality metrics.
• Learning‑in‑the‑loop – demonstrates that agents can accumulate corrective knowledge across problems, improving over time without retraining the base model.
• Open‑source artifacts – code, prompts, and a benchmark harness released for reproducibility and community extension.

Methodology

1. Model selection – Small models (e.g., LLaMA‑7B, Falcon‑7B) and large baselines (GPT‑4, Claude‑2) are frozen; no fine‑tuning is performed.
2. Agentic workflow – Each design problem is processed through a loop:
   • Decompose the Verilog task into sub‑tasks (specification parsing, module generation, testbench creation).
   • Generate code for each sub‑task using the small model.
   • Validate output with external tools (syntax checkers, simulators).
   • Iterate: if validation fails, the agent receives structured feedback and re‑generates the offending piece.
3. Benchmark harness – The CVDP suite provides 50 real‑world Verilog challenges with ground‑truth solutions and functional correctness metrics.
4. Metrics – Functional correctness (pass/fail), design quality (resource usage, timing), inference latency, GPU memory, and estimated energy consumption.
5. Learning‑in‑the‑loop – A lightweight memory store retains successful patterns and error corrections, which are injected as context in subsequent runs.

Results & Findings

• The agentic pipeline closes > 80 % of the performance gap between small and giant models.
• Energy consumption drops by ~80 %, making the approach viable for on‑premise or edge deployment.
• Iterative feedback reduces syntax‑error rates from > 30 % (single‑shot) to < 5 % after two correction cycles.
• The memory‑augmented agent improves over a sequence of problems, shaving ~0.3 s per task after the first 10 designs.

Practical Implications

• Cost‑effective hardware automation – Companies can embed small‑model agents in CI pipelines for Verilog generation, verification, and refactoring without provisioning expensive GPU clusters.
• Sustainable AI – Lower energy footprints align with corporate ESG goals and reduce operational expenditures for design houses.
• Rapid prototyping – The modular agentic framework can be swapped into existing EDA tools, enabling “AI‑assist” features (e.g., auto‑completion, bug‑fix suggestions) on modest workstations.
• Edge‑ready design assistants – Small models fit on a single high‑end GPU or even a CPU‑only server, opening the door for on‑site AI assistance in secure or air‑gapped environments.
• Transferable workflow – The same decomposition‑feedback loop can be adapted to other hardware description languages (VHDL, SystemVerilog) or even software code generation tasks.

Limitations & Future Work

• Domain coverage – Experiments focus on Verilog; broader HDL ecosystems and mixed‑signal designs remain untested.
• Memory scaling – The current knowledge store is a simple key‑value cache; more sophisticated retrieval‑augmented models could boost long‑term learning.
• Tool integration overhead – Validation steps (simulation, synthesis) dominate runtime; tighter coupling with EDA APIs could reduce latency.
• Robustness to ambiguous specs – The agentic pipeline still struggles when the problem statement is underspecified; future work will explore prompting strategies and external knowledge bases.
• Scaling to larger design suites – While the approach works on 50 benchmark problems, real‑world chip projects involve thousands of modules; hierarchical agent orchestration is a promising direction.

Authors

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

Paper Information

• arXiv ID: 2512.05073v1
• Categories: cs.LG, cs.AI, cs.AR, cs.SE
• Published: December 4, 2025
• PDF: Download PDF