# DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators

arXiv: https://arxiv.org/abs/2201.11220 · HTML: https://mlsanity.com/paper/2201.11220/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning](https://mlsanity.com/paper/2009.02010/) (2020)

## Built on by (most cited first, 7 of 7)

- [DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators](https://mlsanity.com/paper/2509.10702/) (2025)
- [Demystifying Map Space Exploration for NPUs](https://mlsanity.com/paper/2210.03731/) (2022)
- [Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators](https://mlsanity.com/paper/2409.02817/) (2024)
- [AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations](https://mlsanity.com/paper/2501.09954/) (2025)
- [CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design](https://mlsanity.com/paper/2506.03474/) (2025)
- [DEAP: Design Space Exploration for DNN Accelerator Parallelism](https://mlsanity.com/paper/2312.15388/) (2023)
- [Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability](https://mlsanity.com/paper/2504.09851/) (2025)

## Abstract (arXiv)

The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy. Intensive research has been conducted to optimize each of them independently. Unfortunately, optimizing for both together is extremely challenging due to the extremely large cross-coupled search space. To address this, in this paper, we propose a HW-Mapping co-optimization framework, an efficient encoding of the immense design space constructed by HW and Mapping, and a domain-aware genetic algorithm, named DiGamma, with specialized operators for improving search efficiency. We evaluate DiGamma with seven popular DNNs models with different properties. Our evaluations show DiGamma can achieve (geomean) 3.0x and 10.0x speedup, comparing to the best-performing baseline optimization algorithms, in edge and cloud settings.