# OpenACMv2: An Accuracy-Constrained Co-Optimization Framework for Approximate DCiM

arXiv: https://arxiv.org/abs/2603.13042 · HTML: https://mlsanity.com/paper/2603.13042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [A Review of SRAM-based Compute-in-Memory Circuits](https://mlsanity.com/paper/2411.06079/) (2024)
- [OpenACM: An Open-Source SRAM-Based Approximate CiM Compiler](https://mlsanity.com/paper/2601.11292/) (2026)

## Abstract (arXiv)

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 show that the proposed two-level ACCO framework achieves most of its accuracy-constrained efficiency gain at Level-1 through architecture exploration, delivering roughly 50%+ PDP reduction, while Level-2 transistor-level optimization provides a further single-digit PDP improvement while preserving accuracy, enabling rapid "what-if" exploration for approximate DCiM. The framework is available on GitHub (https://github.com/ShenShan123/OpenACM).