{"slug": "ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation", "title": "TernScope: Model-Hardware Co-Design for Efficient Ternary Endoscopic Segmentation", "summary": "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.", "body_md": "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", "url": "https://wpnews.pro/news/ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation", "canonical_source": "https://aiflash.com/news/143918/", "published_at": "2026-11-02 07:30:23+00:00", "updated_at": "2026-11-02 07:50:05.325523+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research", "ai-infrastructure"], "entities": ["TernScope"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation", "markdown": "https://wpnews.pro/news/ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation.md", "text": "https://wpnews.pro/news/ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation.txt", "jsonld": "https://wpnews.pro/news/ternscope-model-hardware-co-design-for-efficient-ternary-endoscopic-segmentation.jsonld"}}