arXiv — NLP / Computation & Language · · 3 min read

LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Computer Science > Hardware Architecture

arXiv:2608.02915 (cs)
[Submitted on 3 Aug 2026]

Title:LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension

View a PDF of the paper titled LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension, by Pingqing Zheng and 7 other authors
View PDF
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 this https URL.
Subjects: Hardware Architecture (cs.AR); Computation and Language (cs.CL); Software Engineering (cs.SE)
Cite as: arXiv:2608.02915 [cs.AR]
  (or arXiv:2608.02915v1 [cs.AR] for this version)
  https://doi.org/10.48550/arXiv.2608.02915
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiayin Qin [view email]
[v1] Mon, 3 Aug 2026 22:07:06 UTC (3,187 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension, by Pingqing Zheng and 7 other authors
  • View PDF
  • TeX Source

Current browse context:

cs.AR
< prev   |   next >
Change to browse by:

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language