The AIE-ML Development Feature Tutorials highlight specific features and flows that help develop AIE-ML applications.
| Tutorial | Description |
| A to Z Bare-metal Flow | This tutorial walks through the steps to create a custom Baremetal platform, and also integrate Baremetal host application along with an AI Engines graph and PL kernels. |
| Using GMIO with AIE-ML | This tutorial introduces the usage of global memory I/O (GMIO) for sharing data between the AI Engine-ML (AIE-ML) and external DDR. |
| Runtime Parameter Reconfiguration | Learn how to dynamically update AI Engine-ML (AIE-ML) runtime parameters. |
| Packet Switching | This tutorial illustrates how to use data packet switching with AI Engine-ML (AIE-ML) designs to optimize efficiency. |
| AI Engine Versal Integration for Hardware Emulation and Hardware | This tutorial demonstrates creating a system design running on the AI Engine-ML (AIE-ML), PS, and PL and validating the design running on these heterogeneous domains by running Hardware Emulation. |
| AI Engine-ML Performance Analysis Tutorial | This tutorial introduces you to performance analysis and optimization methods, and shows you how synchronization works in graph execution. It also demonstrates the analysis of a hang issue using an example. |
| AIE Compiler Features | This tutorial shares a variety of features that are useful for AI Engine / AI Engine-ML (AIE-ML) programming to create more visible and efficient code compared to early versions of the compiler. |
| Matrix Compute with Vitis Libraries | In this tutorial, we explore how to use matrix multiplication/General Matrix Multiply (GEMM) from the DSP Vitis library. We will examine various design requirements and configure the parameters accordingly. Finally, we will migrate the design to the AIE-ML architecture and compare its performance with AIE architecture. |
| Tiling Parameter Programming | In this tutorial you will learn how to use a major feature of AI Engine-ML devices: tiling parameters.These parameters can be used at all memory levels of the AI Engine-ML: Local memory (memory modules) with the kernel io-buffers, memory tiles (shared memory in adf language) which are used as large intermediate memories in the system and external memory for DDR access. |
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