PAI-120 · Education

Silicon for Physical AI

Design the silicon that runs Physical AI, from a single NAND gate to an edge-AI accelerator, then out to a real chip. Logic, synthesis, timing, quantization, AI-assisted design, and the economics of getting made, with TinyTapeout as the real-fab capstone.

Beginner → Skilled·6 modules·11 lessons
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THE HORIZON

Where this sits, and what moves it.

Binding constraint · The critical path, and the economics of the mask set. Timing decides what a chip can do; the mask decides whether it is ever made.

Was impossible

Designing silicon meant a company. A student could learn the theory and never see a fabricated part, so the loop from a logic decision to a physical die never closed.

Is probable

Open tooling and a shared shuttle close it, and this course ends at a real fab. AI writing RTL is arriving fast, and the course puts the weight where it belongs: verification is the bottleneck, and a generated design you cannot check is a liability rather than a speedup.

Becomes possible

The interesting question is not whether AI writes more RTL -- it will -- but whether the checking scales with it. Watch formal coverage per engineer-hour. If that curve does not bend, generated hardware slows the field down, and if it does, a student with a shuttle slot can do something that used to need a fab.

Every hard thing was impossible until the constraint that made it impossible was named. How we read a frontier →

Module 4

AI-Assisted Design

Use the way the industry is actually heading (AI writes the RTL, you verify it) and learn why the verification, not the generation, is the skill.

  1. L4Verify the MachineYou test AI-generated RTL and your tests pass, but a bug survives. Where was it hiding?Catch a bug in AI-generated RTL by writing a testbench, the verification-first discipline that makes AI-assisted design usable.