This repository develops a complete GPU architecture in Verilog targeting machine learning acceleration. The GPU uses a RISC-V-derived internal ISA modified for GPU contexts and focuses on ML-critical operations like BF16 arithmetic, exp, log, tanh, and sqrt to minimize die area. The project aims to provide PyTorch compatibility via HIP/CUDA APIs and includes C++ single-source compilation infrastructure for simulation.