What question did this study set out to answer?

This research aims to unify prior findings in Tensor-Native Semantic Computing and demonstrate their application.

April 1, 2026Open Access

From Semantic Impedance to Measurable Elimination: A Formal Closure of the Tensor-Native Semantic Computing Series with Unified Equations, Hardware Projections, and AI Convergence

Key Points

This research aims to unify prior findings in Tensor-Native Semantic Computing and demonstrate their application.
Introduced the Master Equation to consolidate TNSC principles into a single formula.
Provided quantitative hardware performance projections.
Established a formal proof relating TNSC to AI workloads.
Achieved a 20x reduction in PCIe usage on specific hardware.
Demonstrated an 18x reduction in VRAM requirements with no hardware upgrades.
Verified that TNSC principles are applicable to AI inference and rendering tasks.

Abstract

Final paper in the eight-paper Tensor-Native Semantic Computing (TNSC) series. Presents three contributions: (1) the Master Equation of TNSC unifying all prior layers into a single formula; (2) quantitative hardware projections showing 20x PCIe reduction and 18x VRAM reduction on hardware as old as the GTX 1060 6GB with no hardware changes; (3) a formal proof that TNSC principles apply isomorphically to AI inference and rendering workloads. Includes a complete cross-reference index mapping every equation in the series to its origin paper.

From Semantic Impedance to Measurable Elimination: A Formal Closure of the Tensor-Native Semantic Computing Series with Unified Equations, Hardware Projections, and AI Convergence

Key Points

Abstract

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