Ternary Post-Training Quantization
A master teaches a student in three values — knowledge distilled to its essence.
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.
A master teaches a student in three values — knowledge distilled to its essence.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A large, luminous floating-point model pouring its knowledge downward into a small, crystalline ternary student. The knowledge transforms as it falls — continuous gradients becoming discrete choices: yes, no, and silence. Everything essential survives the compression.
Abstract
Fast, iterative post-training quantization of full-precision models to ternary representation without retraining, preserving >95% model accuracy.
Visual Essence
A large, luminous floating-point model pouring its knowledge downward into a small, crystalline ternary student. The knowledge transforms as it falls — continuous gradients becoming discrete choices: yes, no, and silence. Everything essential survives the compression.
Technology Domains
Related Patents
From the compression-crystal visual family
Batch Normalization in Ternary Quantized Networks
Prune the tree, then ternarise what remains — 200× smaller models.
Recurrent Neural Network Inference in Ternary Domain
Gradients compressed to three values — 8× less bandwidth across the training cluster.
Agent Marketplace and Resource Trading
The ACE conversion engine — any model to ternary, automatically, with benchmark feedback.
Agricultural Precision Sensing via Ternary CNN
Any model, any framework — automatic conversion to ternary deployment.