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To provide good classification accuracy and meet challenging design constraints, image classifiers deployed in embedded systems are typically implemented using convolutional neural networks (CNNs) accelerated with specialized inference accelerators. We propose a supernet-based neural architecture search (NAS) method to automate the design process of CNNs executed on the Hailo-8L accelerator, an expanding module for the Raspberry Pi Kit. The method utilizes a multi-objective genetic algorithm in conjunction with a pre-trained supernet and latency predictor to accelerate the evaluation of candidate CNNs. The latency predictor is trained using a collection of generated CNNs whose exact latency is determined directly on the Hailo-8L accelerator. The method is evaluated using CIFAR-100 and ImageNet-100 benchmarks and provides classifiers with competitive accuracy-latency trade-offs.

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Automated Design of CNN-Based Image Classifiers for Hailo-8L Accelerator

  • 会议论文

  • 发表日期：

    2026年

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  • David Nepras,
  • Lukas Sekanina

摘要

To provide good classification accuracy and meet challenging design constraints, image classifiers deployed in embedded systems are typically implemented using convolutional neural networks (CNNs) accelerated with specialized inference accelerators. We propose a supernet-based neural architecture search (NAS) method to automate the design process of CNNs executed on the Hailo-8L accelerator, an expanding module for the Raspberry Pi Kit. The method utilizes a multi-objective genetic algorithm in conjunction with a pre-trained supernet and latency predictor to accelerate the evaluation of candidate CNNs. The latency predictor is trained using a collection of generated CNNs whose exact latency is determined directly on the Hailo-8L accelerator. The method is evaluated using CIFAR-100 and ImageNet-100 benchmarks and provides classifiers with competitive accuracy-latency trade-offs.