Ternary Graph Neural Networks
Today's commercial NPUs run ternary models through translation — no hardware changes.
Explore the Vision
Discover this technology through five complementary perspectives — from technical architecture to partnership outcomes. Each layer reveals a different aspect of how this innovation creates value.
Today's commercial NPUs run ternary models through translation — no hardware changes.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A commercial neural processing unit — recognisable, shipping today — receiving ternary weight streams through a translation layer that maps {-1, 0, +1} to the chip's native format. The hardware doesn't know it's running ternary. The software makes it invisible.
Abstract
Graph neural network architectures quantized to ternary domain for modeling structured data and relational information efficiently.
Visual Essence
A commercial neural processing unit — recognisable, shipping today — receiving ternary weight streams through a translation layer that maps {-1, 0, +1} to the chip's native format. The hardware doesn't know it's running ternary. The software makes it invisible.
Technology Domains
Related Patents
From the silicon-awakening visual family
Ternary Neural Processing Unit Architecture for Binary NPU Optimization
Existing chips run ternary — no new silicon required.
Zero-Skip Gating for Ternary Neural Networks
Normalisation layers dissolve into the ternary fabric — no floating-point tax.
Ternary Weight Pruning and Sparsification
Weights and activations co-designed — the whole pipeline speaks three values.
Mixed-Precision Ternary Inference Scheduling
The architecture searches itself — evolution finds the optimal ternary shape.