[2606.02672] Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow

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:2606.02672 (cs)

[Submitted on 1 Jun 2026]

Title:Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow

Authors:Corey Lammie<br>View a PDF of the paper titled Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow, by Corey Lammie

View PDF<br>HTML (experimental)

Abstract:Analog In-Memory Computing (AIMC) accelerators execute matrix-vector multiplications directly within memory arrays, reducing data movement and improving DNN inference efficiency. Their limited effective precision motivates heterogeneous architectures that combine analog compute tiles with digital processing units. This letter classifies existing methods for partitioning DNN workloads across these resources by mapping granularity, optimization strategy, and model support, and distills them into a unified four-stage workflow. To demonstrate the workflow on a model class not yet addressed by existing methods, we apply its first two stages to GPT-2, producing the first AIMC-specific precision sensitivity profile for a decoder-only transformer. Sensitivity is dominated by 4 of 49 projections, with the first decoder block's attention output dominating by an order of magnitude. This suggests that projection-level mapping and selective digital execution of early-block and output-facing projections are important for reliable decoder-transformer deployment on AIMC hardware.

Comments:<br>Accepted by IEEE Computer Architecture Letters

Subjects:

Hardware Architecture (cs.AR); Emerging Technologies (cs.ET)

Cite as:<br>arXiv:2606.02672 [cs.AR]

(or<br>arXiv:2606.02672v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2606.02672

Focus to learn more

arXiv-issued DOI via DataCite

Journal reference:<br>IEEE Computer Architecture Letters 2026

Related DOI:

https://doi.org/10.1109/LCA.2026.3699929

Focus to learn more

DOI(s) linking to related resources

Submission history<br>From: Corey Lammie [view email]<br>[v1]<br>Mon, 1 Jun 2026 12:17:51 UTC (99 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow, by Corey Lammie<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.AR

next >

new<br>recent<br>| 2026-06

Change to browse by:

cs<br>cs.ET

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