[2604.13523] TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Hardware Architecture

arXiv:2604.13523 (cs)

[Submitted on 15 Apr 2026 (v1), last revised 12 Jul 2026 (this version, v2)]

Title:TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting

Authors:Ruijie Gao, Haoran Jin, Jirong Yang, Nathaniel Bleier<br>View a PDF of the paper titled TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting, by Ruijie Gao and 3 other authors

View PDF<br>HTML (experimental)

Abstract:Numerous tensor accelerator designs have been proposed, yet most lack well-documented ISAs and compiler backends, limiting evaluation to a handful of operators. Recent work has shown that given a tensor-level ISA specification, complete software stacks including compiler backends can be automatically generated--but writing such specifications remains a manual, expert-driven process.

We present ATLAAS, the first end-to-end MLIR-based pipeline that lifts RTL-extracted accelerator semantics to tensor ISA specifications. Starting from bit-level LLVM IR produced by prior architecture-level model extraction, ATLAAS applies an 8-pass semantic lifting pipeline that progressively recovers high-level tensor structure--MAC idioms, saturation semantics, multi-dimensional buffer organizations, and data layout transformations--emitting specifications that immediately enable automatic software stack generation through the ACT ecosystem.

We evaluate ATLAAS on the Gemmini systolic-array accelerator, where the pipeline collapses bit-level MLIR by up to 92.9% on processing elements and 24-34% on controller modules. ATLAAS discovers hardware features omitted from the hand-written reference, with correctness validated via Z3 SMT equivalence proofs. Generality is confirmed on TVM's VTA processor, where the same pipeline lifts all four datapath modules without accelerator-specific changes, enabling an automated path from RTL to a performance-competitive compiler backend.

Comments:<br>Accepted by ICCAD '26, this is not the camera-ready version

Subjects:

Hardware Architecture (cs.AR)

Cite as:<br>arXiv:2604.13523 [cs.AR]

(or<br>arXiv:2604.13523v2 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2604.13523

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Ruijie Gao [view email]<br>[v1]<br>Wed, 15 Apr 2026 06:15:56 UTC (1,502 KB)

[v2]<br>Sun, 12 Jul 2026 19:35:34 UTC (1,897 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting, by Ruijie Gao and 3 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.AR

next >

new<br>recent<br>| 2026-04

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

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?)

Author

Venue

Institution

Topic

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 .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

Major funding support from