Modern AI workloads shouldn’t need to be rewritten for every device. Yet today, performance still depends heavily on vendor-specific frameworks, driver stacks, and hand-tuned kernels.
U-HOP (Universal Hardware Optimization Protocol) is an open initiative to break that dependency by creating a unified optimization layer that lets compute run fast anywhere.
Write once → run optimized across GPUs, CPUs, NPUs, TPUs, and edge accelerators.
What U-HOP Does
U-HOP dynamically selects the best compute backend and generates optimized kernels for the underlying hardware — automatically.
Think of it as:
A protocol that maps high-level ops to the best low-level execution path available at runtime.
Initial focus areas:
• Matrix operations (matmul)
• Conv2D ops
• ReLU / activation pipelines
• Device introspection + runtime backend selection
• Foundations for future AI-generated kernel synthesis
Why This Matters
We’re moving toward a world where models run:
• On multi-GPU rigs
• On phones with NPUs
• On browser WebGPU
• On edge compute like Jetson / RK3588
• On future AI accelerators
Fragmentation limits innovation.
U-HOP’s goal is to unify compute execution and unlock “write once, run fast anywhere” for ML workloads — starting with real operator-level performance wins.
Current Status (MVP Phase)
• Runtime architecture defined
• Backend probing + dispatch in progress
• Core op specification (v0.1) drafted
• First demos in pipeline:
• matmul across heterogeneous devices
• ReLU + Conv2D proof runs
• Benchmarking vs naive exec paths
Next milestone: AI-generated kernel optimization demo.
Repo:
github.com/sevenloops/uhop (active early-stage build)
Get Involved
We’re building in the open. If you’re passionate about:
• GPU architecture
• Kernel optimization
• Runtime compilers
• ONNX / CUDA / ROCm / WebGPU
• Edge acceleration
• AI-generated system code
We’d love to collaborate.
Comment. PR. Fork. Stress-test. Let’s build a new standard.
Vision
A protocol layer that eventually becomes the bridge between AI→hardware, enabling models — and future AI compilers — to target any compute substrate without rewriting code.
Hardware becomes a capability layer, not a constraint.
U-HOP is a first step toward that future.
Call to action
Clone the repo & try the early dispatch tests:
git clone https://github.com/sevenloops/uhop
cd uhop
python tests/dispatch_demo.py
Share feedback, ideas, challenges, and benchmarks.
Let’s shape the protocol together.
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