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The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance. Compilation and minimal runtimes commonly unlock ML workloads on existing hardware. Automatically generate and optimize tensor operators on more backends. Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above? NNVM flexible design enables all of these things and more.
Features
- CPUs, GPUs, browsers, microcontrollers, FPGAs and more
- Automatically generate and optimize tensor operators on more backends
- Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML
- Start using TVM with Python today, build out production stacks using C++, Rust, or Java the next day
- Compilation and minimal runtimes commonly unlock ML workloads on existing hardware
- Compilation of deep learning models into minimum deployable modules
Project Samples
