AI_Engine_Development

2020.2 Vitis™ - AI Engine Development Tutorials See Vitis™ Development Environment on xilinx.com

Design Tutorials

The methodology for developing optimized accelerated applications is comprised of two major phases: architecting the application, and developing the kernels. In the first phase, you make key decisions about the application architecture by determining which software functions should be accelerated onto ACAP kernels, how much parallelism can be achieved, and how to deliver it in code. In the second phase, you implement the kernels by structuring the source code, and applying the necessary build options s to create the kernel architecture needed to achieve the optimized performance target. The following examples illustrate the use of this methodology in real-world applications.

Tutorial	Description
LeNet Tutorial	This tutorial uses the LeNet algorithm to implement a system-level design to perform image classification using the AI Engine and PL logic, including block RAM (BRAM). The design demonstrates functional partitioning between the AI Engine and PL. It also highlights memory partitioning and hierarchy among DDR memory, PL (BRAM) and AI Engine memory..
Super Sampling Rate FIR Filters	The purpose of this tutorial is to provide a methodology to enable you to make appropriate choices depending on the filter characteristics, and to provide examples on how to implement Super Sampling Rate (SSR) FIR Filters on a Versal ACAP AI Engine processor array.

Feature Tutorials

Tutorial	Description
A to Z Bare-metal Flow	This tutorial introduces a complete end to end flow for a bare-metal host application using AI Engines and PL kernels.

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