mlsanityData to 14 Oct 2026

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

arXiv 2201.11220 ↗First posted 26 Jan 2022cs.NE cs.AIcited by 7

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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.

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

3 of 11 references.

References · 3

  1. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  2. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 150
  3. 2020ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learningcited by 35

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 4.

7 papers.

  1. 2025DOSA: Differentiable Model-Based One-Loop Search for DNN Acceleratorscited by 4
  2. 2022Demystifying Map Space Exploration for NPUscited by 2
  3. 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Acceleratorscited by 1
  4. 2025AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representationscited by 1
  5. 2025CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Designcited by 1
  6. 2023DEAP: Design Space Exploration for DNN Accelerator Parallelismcited by 0
  7. 2025Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainabilitycited by 0