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:

• Verilog RTL ready for synthesis on 28nm/22nm
• Three tiered implementations for different power/cost targets
• DAO-governed funding and roadmap
• Built-in agent runtime support (secure enclave, persistent KV-cache)

Get Involved

GitHub: https://github.com/JesesePU/tsu-protocol
Website: https://landing-ivory-theta.vercel.app
Donate (TRC-20 USDT): TU8NBT5iGyMNkLwWmWmgy7tFMbKnafLHcu

Contributors, sponsors, and collaborators welcome. The future of AI hardware should be open.