There are over 2.5 billion Qualcomm processors in the world today (PC, mobile, automotive, etc). But the process for bringing AI models to run on Qcom processors is a (massive) pain. Their 2GB+ SDK is an encyclopedia's worth of information needed to deploy correctly.
We're working to make Qualcomm NPUs a first-class citizen for deployment from PyTorch. Devs can write a Python function that runs a PyTorch model, then use our `@compile` decorator to transpile the model to a Qcom-specific C++ implementation (DLC) which compiles to a self-contained shared library.
The Qualcomm NPUs are fast. 1.8x faster than ONNXRuntime. See the link above.
There are over 2.5 billion Qualcomm processors in the world today (PC, mobile, automotive, etc). But the process for bringing AI models to run on Qcom processors is a (massive) pain. Their 2GB+ SDK is an encyclopedia's worth of information needed to deploy correctly.
We're working to make Qualcomm NPUs a first-class citizen for deployment from PyTorch. Devs can write a Python function that runs a PyTorch model, then use our `@compile` decorator to transpile the model to a Qcom-specific C++ implementation (DLC) which compiles to a self-contained shared library.
The Qualcomm NPUs are fast. 1.8x faster than ONNXRuntime. See the link above.