Agents that route PCBs through KiCad's native API with design-rule feedback outperform grid-action RL and open-loop LLM baselines, and a synthetic-trained PPO transfers zero-shot to real boards.
Meic: Re-thinking rtl debug automation using llms
6 Pith papers cite this work, alongside 25 external citations. Polarity classification is still indexing.
6
Pith papers citing it
25
external citations · external index
representative citing papers
Clover fixes 96.8% of bugs on an RTL-repair benchmark using stochastic tree-of-thoughts and neural-symbolic agents, outperforming traditional and LLM baselines by 94% and 63% respectively with 87.5% pass@1.
HCM estimates uncertainty in neural network outputs by quantifying violation of a unit hypersphere constraint on the normalized direction vector.
Spec2Cov uses an LLM agent in a feedback loop with a hardware simulator to generate tests from specs, achieving 100% coverage on simple designs and up to 49% on complex ones across 26 benchmarks.
UVM^2 is an LLM-driven system that generates and refines UVM testbenches for RTL verification, reporting up to substantial time savings and average code/function coverage of 87.44%/89.58% on designs up to 1.6K lines, outperforming prior methods.
A 65 nm compute-in-memory chip implements multi-modal Bayesian neural networks with a calibration-free GRNG to deliver risk-aware skin lesion screening with reported gains in coverage, robustness, and efficiency over unimodal baselines.
citing papers explorer
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PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
Agents that route PCBs through KiCad's native API with design-rule feedback outperform grid-action RL and open-loop LLM baselines, and a synthetic-trained PPO transfers zero-shot to real boards.
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Clover: A Neural-Symbolic Agentic Harness with Stochastic Tree-of-Thoughts for Verified RTL Repair
Clover fixes 96.8% of bugs on an RTL-repair benchmark using stochastic tree-of-thoughts and neural-symbolic agents, outperforming traditional and LLM baselines by 94% and 63% respectively with 87.5% pass@1.
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Uncertainty Estimation via Hyperspherical Confidence Mapping
HCM estimates uncertainty in neural network outputs by quantifying violation of a unit hypersphere constraint on the normalized direction vector.
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Spec2Cov: An Agentic Framework for Code Coverage Closure of Digital Hardware Designs
Spec2Cov uses an LLM agent in a feedback loop with a hardware simulator to generate tests from specs, achieving 100% coverage on simple designs and up to 49% on complex ones across 26 benchmarks.
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From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification
UVM^2 is an LLM-driven system that generates and refines UVM testbenches for RTL verification, reporting up to substantial time savings and average code/function coverage of 87.44%/89.58% on designs up to 1.6K lines, outperforming prior methods.
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A 65 nm Multi-Modal Bayesian Inference Engine with 16.3 fJ/Sample Calibration-Free GRNG for Risk-Aware At-Home Skin Lesion Screening
A 65 nm compute-in-memory chip implements multi-modal Bayesian neural networks with a calibration-free GRNG to deliver risk-aware skin lesion screening with reported gains in coverage, robustness, and efficiency over unimodal baselines.