Versal adaptive compute acceleration platforms (ACAPs) combine Scalar Engines, Adaptable Engines, and Intelligent Engines with leading-edge memory and interfacing technologies to deliver powerful heterogeneous computing for any application. Most importantly, Versal ACAP hardware and software are targeted for programming and optimization by data scientists as well as software and hardware developers.
Some Versal ACAP devices incorporate an array of very-long instruction word (VLIW) processors with single instruction multiple data (SIMD) vector units called, AI Engines, that are highly optimized for compute-intensive applications such as 5G wireless and artificial intelligence (AI) applications. When implementing an algorithm for a Versal AI Core or Versal AI Edge, it is important to understand what the AI Engine does well and what would be better implemented in the other engines, for example, the Scalar and Adaptable engines.
Embedded system designs using Versal AI Core devices can include AI Engine graph applications developed and tested using Vitis tools. The figure above shows the development flow for AI Engine graph applications in which individual kernel code is compiled and combined into the graph application.
As described in
AI
Engine Kernel and Graph Programming Guide (UG1079), an
AI Engine kernel is a C/C++ program written
using specialized intrinsic calls that target the VLIW SIMD vector processor. The
AI Engine kernel code is compiled using the
AI Engine compiler (aiecompiler) to compile the kernels to produce an ELF file that is run on
the AI Engine processors.
An AI Engine program requires a data flow graph specification which is written in C++. This specification can be compiled and executed using the AI Engine compiler. An adaptive data flow (ADF) graph application consists of nodes and edges where nodes represent compute kernel functions, and edges represent data connections. The ADF graph is a static dataflow graph with the AI Engine kernels operating in parallel. Kernels operate on data streams, and are the fundamental building blocks of an ADF graph specification. These kernels consume input blocks of data and produce output blocks of data. Refer to AI Engine Kernel and Graph Programming Guide (UG1079) for more information.
As discussed in
Using the Vitis
IDE
in UG1076, an AI Engine
project can be built in the Vitis IDE as an
Application project targeting the aiengine domain of a
Versal AI Core device. The project can also be
managed from the command line. The kernels and ADF graph are written as C++ code and
compiled using the aiecompiler command as described in
Compiling an AI Engine Graph Application,
and simulated using the x86simulator or aiesimulator as described in Simulating an AI Engine Graph
Application.
By default, the AI Engine compiler writes all outputs to a directory called
./Work and creates a file called libadf.a, where Work is
a sub-directory of the current directory where the tool was launched, and libadf.a is written to the same directory as the AI Engine
compiler was launched from and is used for linking with PL kernels and the extensible
platform using the v++ command as explained in the
next section.