deterministic oracle for hardware designs with replayable proofs
Fingerprints hardware interaction graphs to catch trojans functional testing misses.

Simulating a 16,777,216-MAC analog in-memory Phoenix SoC on a $60 Android using iverilog and Yosys is an audacious technical flex — the repo claims end-to-end verification (adders → ALUs → RISC‑V core → FPU) and a working 8-bit CPU. It's simulation-only and the writeup could do more to make reproduction trivial, but the ULA plain-English compiler and the no-cloud, mobile-only build story make this a rare outsider contribution to EDA.
Chip designers, semiconductor / EDA engineers, RISC‑V and hardware hackers, systems architects interested in in-memory compute
Phoenix 4096×4096 specs: - 16,777,216 MAC units (analog in-memory compute) - 50,331 TOPS @ 3GHz (25× NVIDIA H100) - 512 deterministic reasoning cores - 2 TB/s memory bandwidth - All verified in simulation
Hardware verified (15/15 passing): - Half Adder → Full Adder → 8/16/64/128/256-bit ALUs - 8-bit CPU (Fibonacci working) - RISC-V core - FPU (IEEE 754) - GPU SIMD - Complete Phoenix SoC
Also built: - ULA compiler: Plain English → 12 languages (Python, Rust, Java, Assembly, COBOL, Fortran, C, C++, Go, Kotlin, Solidity, TypeScript) - RF presence detection: 2-person tracking via WiFi signals, no cameras, 91% gesture accuracy
All code on GitHub: https://github.com/jltackett1980-cell
Built in Sikeston, MO on a $60 Android. No laptop. No cloud. No funding.
Looking for: Chip designers, semiconductor engineers, anyone interested in open silicon or analog computing. Want to tape out with Efabless or similar.
Live demo available
Fingerprints hardware interaction graphs to catch trojans functional testing misses.
Piping accelerometer data through audio filters to flash keyboard lights is genuinely wild.
Turns a friend's Android into a geo-spoofing exit node without paying for a VPN.
TPM-anchored agent identity solves a real problem, but product is vaporware—coming soon, no code yet.
Accessibility tree + LLM loop beats vision-first approaches for reliable mobile automation.
Android accessibility automation via LLM, but Anthropic's computer-use model already does this.