Ternary Tensor Decomposition and Factorization
The compiler knows the hardware — scheduling ternary execution across heterogeneous cores.
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 compiler knows the hardware — scheduling ternary execution across heterogeneous cores.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A compiler's view of a heterogeneous chip — ternary cores, conventional cores, memory banks, buses — all coordinated by an intelligent scheduler that maps ternary operations to their optimal hardware home. The software understands the silicon.
Abstract
Low-rank tensor decomposition methods for further compressing ternary models while maintaining inference accuracy within specification.
Visual Essence
A compiler's view of a heterogeneous chip — ternary cores, conventional cores, memory banks, buses — all coordinated by an intelligent scheduler that maps ternary operations to their optimal hardware home. The software understands the silicon.
Technology Domains
Related Patents
From the silicon-blueprint visual family
Ternary NPU Compiler Optimization Passes
The ternary chip itself — a complete microarchitecture specification.
Ternary Batch Matrix Multiplication (GEMM)
One execution unit handles both ternary and conventional — switchable precision in a single core.
Ternary Model Compression via Knowledge Distillation
Memory redesigned from the ground up for three-valued data.
Hardware-Aware Ternary Network Architecture Search
Ternary data streams through dataflow hardware — bandwidth-optimal inference.