Batch Normalization in Ternary Quantized Networks
Prune the tree, then ternarise what remains — 200× smaller models.
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.
Prune the tree, then ternarise what remains — 200× smaller models.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A vast neural network tree being sculpted by invisible hands — branches that carry no information dissolve into light, while the remaining structure crystallises into three pure states. The tree shrinks 200-fold but its canopy of intelligence remains full. A bonsai of extraordinary density.
Abstract
Optimization of batch normalization for ternary-quantized layers, with techniques for maintaining statistical consistency across {-1, 0, +1} domains.
Visual Essence
A vast neural network tree being sculpted by invisible hands — branches that carry no information dissolve into light, while the remaining structure crystallises into three pure states. The tree shrinks 200-fold but its canopy of intelligence remains full. A bonsai of extraordinary density.
Technology Domains
Related Patents
From the compression-crystal visual family
Ternary Post-Training Quantization
A master teaches a student in three values — knowledge distilled to its essence.
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.