Why Open-Source Hardware Is the Future of AI Inference

The AI industry runs on closed hardware. NVIDIA's CUDA moat. Qualcomm's Hexagon DSP. Apple's Neural Engine. Each is a black box, and each requires licensing fees, NDA agreements, or per-unit royalties just to access.

Open-source software won the server room. Open hardware will win the edge.

The Case for Open-Source Silicon

1. Permissionless Innovation

When hardware is proprietary, you can only run what the vendor allows. Open RTL means you can:

Add custom instructions for your specific model architecture
Remove unused logic to save power and die area
Port to any foundry and any process node
Audit every transistor for security and privacy

2. Economics at Scale

ASIC NRE costs are \$5M-\$50M per tape-out. MPW (multi-project wafer) shuttles cost \$50K-\$200K. Open-source design with MPW manufacturing reduces the barrier to entry by 100x.

3. The RISC-V Precedent

RISC-V proved that open ISA can compete. Today, there are billions of RISC-V cores in production. TSU Protocol extends this to AI inference — defining not just the ISA, but the NPU microarchitecture, the memory hierarchy, and the system integration standard.

Why TSU?

Existing open NPU projects (Systolic Array generators, Gemmini, etc.) are research tools, not production standards. TSU is designed as a manufacturable standard: