NPU Inference Core
Ternary neural network optimisation for binary Neural Processing Units — the foundational stack enabling {-1, 0, +1} inference on existing NPU silicon without hardware modification.
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.
Batch Normalization in Ternary Quantized Networks
Prune the tree, then ternarise what remains — 200× smaller models.
Mixed-Precision Ternary Inference Scheduling
The architecture searches itself — evolution finds the optimal ternary shape.
Cache-Aware Ternary Inference on NPU
Precision shifts on the fly — full power when needed, whisper-quiet when not.
Activation Function Approximation for Ternary Domains
Multiple chips think as one — ternary models spanning beyond a single die.
Ternary Post-Training Quantization
A master teaches a student in three values — knowledge distilled to its essence.
Ternary Convolution Kernel Optimization
The quantisation boundary learns where to draw itself.
Recurrent Neural Network Inference in Ternary Domain
Gradients compressed to three values — 8× less bandwidth across the training cluster.
Attention Mechanism Compression via Ternary Quantization
The transformer attention mechanism — rebuilt for three values.
Ternary NPU Compiler Optimization Passes
The ternary chip itself — a complete microarchitecture specification.
Ternary Sparse Tensor Operations
The nervous system mapped into silicon — biology's architecture in ternary.
Dynamic Precision Selection for Ternary Inference
Zero weights gate their own clocks — 70% of the chip sleeps while the rest thinks.
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.
Ternary Tensor Decomposition and Factorization
The compiler knows the hardware — scheduling ternary execution across heterogeneous cores.
Ternary Graph Neural Networks
Today's commercial NPUs run ternary models through translation — no hardware changes.
Ternary Reinforcement Learning Agents
The scheduler sees the zeros and skips them — sparsity-aware execution on neural engines.
Diffusion Models in Ternary Domain
CPU and NPU collaborate — each layer runs where it fits best.