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University of Central Florida
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CASS-RTL reveals that by harnessing specific attention patterns, LLMs can significantly improve the accuracy of RTL code generation, achieving up to a 20% boost in correctness.
Defend against hardware Trojans in LLM-generated RTL code by structurally and semantically verifying training data, without needing to alter the underlying LLM.