A multi-objective, hardware-aware Neural Architecture Search (NAS) framework is introduced and evaluated for efficient design of convolutional neural networks (CNN) on the Hailo-8L accelerator. The search for CNNs that exhibit good accuracy-latency trade-offs on image classification tasks is conducted using NSGA-II and a single-objective genetic algorithm that imposes a performance constraint. To reduce execution time, we adopt a pretrained supernet, introduce an optimized evolution-training interaction, and develop a latency predictor based on a surrogate model. The framework is primarily intended to construct CNN architectures optimized for Hailo-8L and user-provided application-specific datasets. Nevertheless, to facilitate a direct comparison with existing methods, we evaluated it on standard image classification benchmarks, specifically CIFAR-10, CIFAR-100, and ImageNet-100.