#hardware-automation News & Analysis

4 articles tagged with #hardware-automation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · 4d ago6/10

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PCBSchemaGen: Reward-Guided LLM Code Synthesis for Printed Circuit Boards (PCB) Schematic Design with Structured Verification

Researchers introduce PCBSchemaGen, a training-free framework that enables large language models to generate verified PCB schematics by combining datasheet-derived domain schemas with deterministic verification and Thompson Sampling refinement. The approach achieves 81.3% task success on real IC designs without requiring unit tests or golden references, establishing a general method for LLM code synthesis in domains lacking traditional test oracles.

AINeutralarXiv – CS AI · May 296/10

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SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

SchGen is the first large language model capable of generating editable PCB schematics from natural-language descriptions, addressing a critical gap in hardware design automation. The breakthrough introduces a semantically grounded code representation that transforms geometry-driven design into a semantics-matching task, paired with a large-scale dataset of open-source hardware designs, demonstrating superior accuracy compared to existing LLMs.

AIBullisharXiv – CS AI · May 286/10

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CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts

CircuitLM is a multi-agent AI framework that converts natural language descriptions into machine-readable circuit schematics, addressing persistent hallucination and constraint-violation issues in LLM-based electronic design automation. The system uses a five-stage pipeline combining retrieval-augmented generation with dual-layer verification—electrical rule checking and LLM-as-judge evaluation—to produce structurally viable, prototype-ready circuits.

AIBullisharXiv – CS AI · May 96/10

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LLM-Driven Design Space Exploration of FPGA-based Accelerators

Researchers present SECDA-DSE, an AI-driven framework that integrates Large Language Models into FPGA accelerator design to automate the complex process of hardware configuration optimization. The system combines structured design space exploration with LLM-powered reasoning and feedback loops, demonstrating practical feasibility through successful synthesis on a Zynq-7000 FPGA.