Ternary Convolution Kernel Optimization
The quantisation boundary learns where to draw itself.
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.
The quantisation boundary learns where to draw itself.
What It IS
Technical VisionThe architectural essence — what makes this technology work
Three luminous threshold planes hovering in activation space, drifting and adjusting as data flows through them. Each datum that passes shifts the boundaries slightly — the system teaches itself where +1 ends and 0 begins. Self-calibrating intelligence.
Abstract
Hardware-aware optimization of convolutional kernels in ternary quantization, minimizing data movement and arithmetic operations on NPU.
Visual Essence
Three luminous threshold planes hovering in activation space, drifting and adjusting as data flows through them. Each datum that passes shifts the boundaries slightly — the system teaches itself where +1 ends and 0 begins. Self-calibrating intelligence.
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.