cyclic_add. While AI Engine APIs
support and abstract the main permute use cases, not all permute capabilities are
covered. Using intrinsics may allow you to close the performance gap required by your
An AI Engine kernel is a C/C++ program which is written using native C/C++ language and specialized intrinsic functions that target the VLIW scalar and vector processors. The AI Engine kernel code is compiled using the AI Engine compiler (aiecompiler) that is included in the AMD Vitis™ core development kit. The AI Engine compiler compiles the kernels to produce ELF files that are run on the AI Engine processors.
For more information on intrinsic functions, see the AI Engine Intrinsics User Guide (UG1078). AI Engine compiler and simulator are covered in the first few sections of this chapter.
AI Engine supports specialized data types and intrinsic functions for vector programming. By restructuring the scalar application code with these intrinsic functions and vector data types as needed, you can implement the vectorized application code. The AI Engine compiler takes care of mapping intrinsic functions to operations, vector or scalar register allocation and data movement, automatic scheduling, and generation of microcode that is efficiently packed in VLIW instructions.
The following sections introduce the data types supported and registers available for use by the AI Engine kernel. In addition, the vector intrinsic functions that initialize, load, and store, as well as operate on the vector registers using the appropriate data types are also described.
To achieve the highest performance on the AI Engine, the primary goal of single kernel programming is to ensure that the usage of the vector processor approaches its theoretical maximum. Vectorization of the algorithm is important, but managing the vector registers, memory access, and software pipelining are also required. The programmer must strive to make the data for the new operation available while the current operation is executing because the vector processor is capable of an operation every clock cycle. Optimizations using software pipelining in loops is available using pragmas. For instance, when the inner loop has sequential or loop carried dependencies it might be possible to unroll an outer loop and compute multiple values in parallel. The following sections go over these concepts as well.