Ternary Reinforcement Learning Agents
The scheduler sees the zeros and skips them — sparsity-aware execution on neural engines.
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 scheduler sees the zeros and skips them — sparsity-aware execution on neural engines.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A scheduling engine examining tiles of ternary weights, measuring the density of zeros in each tile, and routing sparse tiles to fast-skip pathways while dense tiles get full computation. The zeros are not computed — they are recognised and honoured.
Abstract
Training and deployment of reinforcement learning agents with ternary policy and value networks on resource-constrained platforms.
Visual Essence
A scheduling engine examining tiles of ternary weights, measuring the density of zeros in each tile, and routing sparse tiles to fast-skip pathways while dense tiles get full computation. The zeros are not computed — they are recognised and honoured.
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.