LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension
By Pingqing Zheng 1, Jiayin Qin 1, Fuqi Zhang 1, Zishen Wan 2, Shang Wu 3, Yu (Kevin) Cao 1, Caiwen Ding 1, Yang (Katie) Zhao 1
1 University of Minnesota, Twin Cities, Minneapolis, Minnesota, USA
2 Columbia University, New York City, New York, USA
3 Northwestern University, Evanston, Illinois, USA
Abstract
Domain-specific Instruction Set Architecture eXtensions (ISAX) are widely adopted in the RISC-V ecosystem to accelerate emerging workloads, but implementing and validating ISAXes across different cores remains slow and fragmented. Existing frameworks still require per-core interface adaptation, and differential testing often breaks once either the microarchitecture or the ISAX changes. We present LACE, an LLM-aided multi-agent workflow that translates natural-language ISAX intents into a compact two-level IR (operation-level and HDL task-level), performs retrieval-guided localized RTL edits over large repositories, and closes the loop with a compiler-agnostic riscv-formal checking flow (assuming RVFI availability or instrumentation). Across four embedded RISC-V cores, LACE raises pass@1 generation accuracy from near-zero to 72.8% within our evaluation setup, while improving code localization and reducing integration rework. The code of LACE is available at https://github.com/UMN-ZhaoLab/LACE.
Index Terms — RISC-V, instruction set extension, large lan guage model, multi-agent system
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