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ARXIV:2603.13042 · COMPUTE OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.13042COMPUTE OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints.
Opportunity summary
Pain OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices.
Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices.
Compute Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints.
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Paper Pack
10.48550/arXiv.2603.13042OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints.
Abstract
Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices. Building on OpenYield, we introduce Accuracy-Constrained Co-Optimization (ACCO) and present OpenACMv2, an open framework that operationalizes ACCO via two-level optimization: (1) accuracy-constrained architecture search of compressor combinations and SRAM macro parameters, driven by a fast GNN-based surrogate for PPA and error; and (2) variation- and PVT-aware transistor sizing for standard cells and SRAM bitcells using Monte Carlo. By decoupling ACCO into architecture-level exploration and circuit-level sizing, OpenACMv2 integrates classic single- and multi-objective optimizers to deliver strong PPA-accuracy tradeoffs and robust convergence. The workflow is compatible with FreePDK45 and OpenROAD, supporting reproducible evaluation and easy adoption. Experiments demonstrate significant PPA improvements under controlled accuracy budgets, enabling rapid "what-if" exploration for approximate DCiM. The framework is available on https://github.com/ShenShan123/OpenACM.
Source availability
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-leve...
METHOD
Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-constrained co-optimization across coupled architecture and transistor-level choices.
WHY NOW
Compute Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
Building on OpenYield, we introduce Accuracy-Constrained Co-Optimization (ACCO) and present OpenACMv2, an open framework that operationalizes ACCO
Explicitly stated in the abstract as the core contribution of the paper.
partial
OpenACMv2, an open framework that operationalizes ACCO via two-level optimization: (1) accuracy-constrained architecture search of compressor combinations and SRAM macro parameters... and (2) variation- and PVT-aware transistor sizing for standard cells and SRAM bitcells
Directly described in the abstract with specific components mentioned.
partial
driven by a fast GNN-based surrogate for PPA and error
Specifically mentioned as part of the architecture search methodology.
partial
variation- and PVT-aware transistor sizing for standard cells and SRAM bitcells using Monte Carlo
Explicitly stated as part of the circuit-level optimization approach.
partial
Experiments demonstrate significant PPA improvements under controlled accuracy budgets
Claimed in the abstract as an experimental result, though specific numbers are not provided in the given text.
partial
enabling rapid 'what-if' exploration for approximate DCiM
Presented as a capability of the framework in the abstract conclusion.
partial
The workflow is compatible with FreePDK45 and OpenROAD, supporting reproducible evaluation and easy adoption
Explicitly stated as a feature supporting reproducibility and adoption.
partial
The framework is available on https://github.com/ShenShan123/OpenACM
Directly stated with specific URL provided.
partial
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Concepts
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OpenACMv2 is an open framework for optimizing power-performance-area in approximate DCiM with accuracy constraints.
Segment
Compute Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
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CITED BY
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status
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reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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FORESIGHT
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TIMELINE
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