Researchers have developed LACE, a novel framework that leverages large language models (LLMs) to streamline the creation and validation of domain-specific instruction set architecture extensions (ISAX) for RISC-V cores. This multi-agent system translates natural language intents into a two-level intermediate representation, performs localized RTL edits on large code repositories, and integrates with a compiler-agnostic formal checking flow. In evaluations across four embedded RISC-V cores, LACE significantly improved generation accuracy to 72.8% at pass@1, enhancing code localization and reducing integration efforts. AI
IMPACT Streamlines hardware design by using LLMs for instruction set extension, potentially accelerating RISC-V adoption.
RANK_REASON The cluster contains a research paper detailing a new framework for hardware architecture development. [lever_c_demoted from research: ic=1 ai=0.7]
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