Frequently Asked Questions

How to Install

See Installing TVM.

How to add a new Hardware Backend

TVM’s relation to Other IR/DSL Projects

There are usually two levels of abstractions of IR in the deep learning systems. TensorFlow’s XLA and Intel’s ngraph both use a computation graph representation. This representation is high level, and can be helpful to perform generic optimizations such as memory reuse, layout transformation and automatic differentiation.

TVM adopts a low-level representation, that explicitly express the choice of memory layout, parallelization pattern, locality and hardware primitives etc. This level of IR is closer to directly target hardwares. The low-level IR adopts ideas from existing image processing languages like Halide, darkroom and loop transformation tools like loopy and polyhedra-based analysis. We specifically focus on expressing deep learning workloads (e.g. recurrence), optimization for different hardware backends and embedding with frameworks to provide end-to-end compilation stack.

TVM’s relation to libDNN, cuDNN

TVM can incorporate these libraries as external calls. One goal of TVM is to be able to generate high-performing kernels. We will evolve TVM an incremental manner as we learn from the techniques of manual kernel crafting and add these as primitives in DSL. See also top for recipes of operators in TVM.


See Security Guide