MLIR unifies the infrastructure for high-performance ML models in TensorFlow.
The MLIR project defines a common intermediate representation (IR) that unifies the infrastructure required to execute high performance machine learning models in TensorFlow and similar ML frameworks. This project will include the application of HPC techniques, along with integration of search algorithms like reinforcement learning. MLIR aims to reduce the cost to bring up new hardware, and improve usability for existing TensorFlow users.
// Syntactically similar to LLVM:
func @testFunction(%arg0: i32) {
%x = call @thingToCall(%arg0) : (i32) -> i32
br ^bb1
^bb1:
%y = arith.addi %x, %x : i32
return %y : i32
}
