SCIENCE@home

GenCNN: A Partition-Aware Multi-Objective Mapping Framework for CNN Accelerators Based on Genetic Algorithm

Yudong Mu,Zhihua Fan,Wenming Li,Zhiyuan Zhang,Xuejun An,Dongrui Fan,Xiaochun Ye

Published 2025 in ACM Transactions on Architecture and Code Optimization (TACO)

ABSTRACT

Convolutional Neural Networks (CNNs) require partitioning to efficiently run on CNN accelerators, which offer multiple parallel processing dimensions, such as Processing Element (PE) array topologies and Single Instruction Multiple Data (SIMD) execution. The choice of parallelization strategy directly impacts accelerator performance. However, the vast search space for CNN partitioning and parallelization makes manual optimization costly and complex, especially when addressing both aspects simultaneously. This highlights the need for an automated framework to efficiently map CNNs onto accelerators. Our key insight is that existing approaches suffer from inadequate accelerator performance modeling and a lack of multi-objective optimization strategies that jointly consider task partitioning and convolution parallelization. To address this, we propose GenCNN, a multi-objective genetic algorithm-based mapping framework for CNN accelerators. GenCNN first constructs a fine-grained performance model that captures both off-chip data access and on-chip data processing. It then applies the Non-dominated Sorting Genetic Algorithm II improved by Multi-Objective Bayesian Optimization to derive a Pareto-optimal partitioning and parallelization strategy that balances off-chip latency and PE utilization. Finally, GenCNN optimizes scheduling and routing to minimize data transfers. Experimental results show that GenCNN achieves up to 17.66× speedup in compilation and 6.47× in execution compared with state-of-the-art mapping frameworks.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    ACM Transactions on Architecture and Code Optimization (TACO)

  • Publication date

    2025-07-23

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1145/3747844
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-46 of 46 references · Page 1 of 1

CITED BY