  Deep neural networks (DNNs) have heavily relied on traditional computational
units like CPUs and GPUs. However, this conventional approach brings
significant computational burdens, latency issues, and high power consumption,
limiting their effectiveness. This has sparked the need for lightweight
networks like ExtremeC3Net. On the other hand, there have been notable
advancements in optical computational units, particularly with metamaterials,
offering the exciting prospect of energy-efficient neural networks operating at
the speed of light. Yet, the digital design of metamaterial neural networks
(MNNs) faces challenges such as precision, noise, and bandwidth, limiting their
application to intuitive tasks and low-resolution images. In this paper, we
propose a large kernel lightweight segmentation model, ExtremeMETA. Based on
the ExtremeC3Net, the ExtremeMETA maximizes the ability of the first
convolution layer by exploring a larger convolution kernel and multiple
processing paths. With the proposed large kernel convolution model, we extend
the optic neural network application boundary to the segmentation task. To
further lighten the computation burden of the digital processing part, a set of
model compression methods is applied to improve model efficiency in the
inference stage. The experimental results on three publicly available datasets
demonstrate that the optimized efficient design improved segmentation
performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from
461.07 MMacs to 166.03 MMacs. The proposed the large kernel lightweight model
ExtremeMETA showcases the hybrid design's ability on complex tasks.
