Show HN: UHOP – Escaping Nvidia Lock-In with an Open Hardware Optimization Layer (www.uhop.dev)

UHOP is an open “hardware optimization layer” that automatically detects available compute devices (CPU, CUDA, OpenCL, ROCm), uses AI to generate optimized kernels for core ML primitives (MatMul, Conv2D, ReLU), benchmarks multiple implementations, validates correctness, and caches the fastest implementation per device configuration for instant reuse. The workflow is Detect → Generate → Optimize → Deploy: UHOP discovers device capabilities, synthesizes candidate kernels, runs real-world performance tests and correctness checks, then persistently stores the best kernel so subsequent runs skip tuning. It presents a unified backend abstraction that can target CPU, CUDA, OpenCL, and ROCm with a single protocol. For the AI/ML community this matters because it addresses vendor lock-in and performance portability: teams can deploy workloads across diverse accelerators without hand-writing device-specific kernels or sacrificing throughput. By automating kernel generation and validation, UHOP can accelerate model deployment on alternative hardware (including emerging accelerators and ROCm ecosystems), improve reproducibility, and lower engineering overhead for optimized ops. Key technical implications include the reliance on AI-driven code synthesis quality, the need to manage correctness and numerical parity across devices, and opportunities to extend the approach to more ops and compiler-level optimizations to close the gap with hand-tuned vendor libraries.