TernScope introduces a model-hardware co-design approach for ternary endoscopic segmentation, pairing a compact low-bit segmentation network with hardware execution simplified by those same low-bit operators. The method uses split-channel large-kernel blocks to reconcile small model size, high accuracy and fast inference for edge medical segmentation. The source provides no measured accuracy, latency, memory or benchmark figures for TernScope.
Edge medical segmentation must reconcile small model size, high accuracy, and fast inference. TernScope addresses this tradeoff through model--hardware co-design, learning a compact segmentation network whose low-bit operators also simplify hardware execution. Split-channel large-kernel blocks prese
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