DIY TPU-on-FPGA meets founder support: tiny accelerators and Grove 2

🧩 The Gist

An open-source project, TinyTinyTPU, implements a TPU‑style 2×2 systolic array for matrix multiply and runs on an FPGA, highlighting accessible experimentation with domain‑specific accelerators. Discussion around the project underscores rising interest in purpose‑built inference hardware. In parallel, OpenAI opened applications for Grove Cohort 2, a five‑week founder program that provides $50K in API credits, early access to tools, and hands‑on mentorship. Together, the updates show momentum at both the hardware and startup enablement layers of the AI stack.

🚀 Key Highlights

• TinyTinyTPU is a TPU‑style matrix‑multiply unit built as a 2×2 systolic array.
• The design is deployed on FPGA, making it practical to prototype and test.
• The project is hosted on GitHub, signaling an open, reproducible implementation.
• Hacker News discussion around the project surfaces interest in specialized inference hardware and comparisons to past GPU‑to‑ASIC shifts.
• OpenAI announced Grove Cohort 2 applications for a five‑week founder program.
• Participants receive $50K in API credits, early access to AI tools, and mentorship from the OpenAI team, open to founders from pre‑idea to product.

🎯 Strategic Takeaways

• Hardware and tooling

  • Small, open FPGA designs that mimic TPU‑style compute make accelerator concepts tangible for developers, educators, and hobbyists.
  • Systolic arrays remain a clear, teachable path to efficient matrix operations that underpin modern ML workloads.

• Startup and ecosystem

  • Grove’s credits, tool access, and mentorship lower early execution risk for founders, especially those validating AI product ideas.
  • Access to cutting‑edge APIs plus structured guidance can compress build cycles from concept to prototype.

• Practical next steps

  • Engineers can use TinyTinyTPU as a hands‑on reference to learn accelerator architecture and test ML kernels.
  • Founders can leverage Grove resources to integrate powerful models early, while focusing limited capital on product fit.

🧠 Worth Reading

• Concept: Systolic array matrix multiply
  Core idea: a grid of simple compute elements passes partial sums and data locally, enabling high‑throughput matrix operations that are central to ML. Practical takeaway: experimenting with a small systolic array on FPGA, like TinyTinyTPU, helps teams understand accelerator trade‑offs and prepares them to target specialized hardware for inference.