AI Engine Rapid Prototyping Overview - AMD introduces the Versal AI Engine Rapid Prototyping using the AMD Vitis Unified IDE for early design analysis and risk reduction [1][2][12][13] - The rapid prototyping feature is available in the Vitis Unified IDE in version 20242 [13][16] Key Steps in Rapid Prototyping - Involves resource estimation, including tile count, buffer usage, PLIO resources, and stream array traffic [2] - Assesses latency and throughput feasibility with early data flow simulations, prototype kernel coding, and initiation interval loop analysis [2] - Utilizes Vitis libraries for existing block elements and develops candidate vectorization options [3] - Includes building empty kernel wrappers, building the graph and compiling, simulating and analyzing for early estimation [12] Custom Kernel Example: Digital Up Conversion (DUC) Chain - The DUC chain translates a signal from baseband to intermediate frequency band and includes a FIR fractional resampler, half-band interpolators, DDS mixer functions, and an adder functional block [5] - The FIR fractional resampler, the half-band interpolators, and DDS mixer functions can be implemented using the Vitis DSP Library [5] - Focuses on fast prototyping of the custom adder kernel, identifying input/output data types, coefficient types, number of taps, sampling rate, and the kernel function [6] AI Engine System Mapping - Identifies hardware resources such as the number of AI Engine tiles, storage, buffers, and connectivity ports [8] - Considers compute (AIE tiles), storage (buffer size, local memory, DMA size), and input/output bandwidth (PLIO ports, clocking, buffer/stream interfaces) [8] - A custom adder kernel requires a sampling rate of 1200 MSPS with a latency less than 500 ns [9] - The adder is implemented in one tile with two inputs and one output of cint16 type, taking 3KB of data at a sampling rate of 1200 MSPS [10][11] Vitis Unified IDE Implementation - Generates data flow models with parametrized kernel ports, multi-core graph topology, full buffering, stream details, and LUT storages [14] - Allows exploration of hardware utilization through AI Engine compilation and ensures throughput and latency requirements are met through AI engine emulation [14] - Requires creating a new empty AI engine component and using the "Generate AIE Prototype Code" option [15] - Involves setting kernel properties such as name, input/output port properties (data type cint16, dimension to 384 samples), and enabling "Generate Top Level graph and Simulation code" [19][20] Simulation and Analysis - The tool generates graph CPP and H files, with the graph CPP setting the graph to run for one iteration (modifiable for better analysis) [20][21] - Requires adding input text files for simulation, containing values representing cint16 samples per clock on the 64-bit interface [22][23][24][25] - Simulation results report a throughput of 5000 megabytes/second or 1250 mega 16-bit complex samples per system, meeting the requirements [26]

Welcome to the AMD Vitis Design Implementation (GitHub) Quick Take video. In this video, we'll quickly introduce GitHub, overview the AMD Vitis DSPLib and its parts, direct you to Vitis tutorials on GitHub, explain polyphase channelizers, and demonstrate a design running in Ubuntu. Let's get started. Let's begin by understanding GitHub.This important website, powered by Git, helps manage project changes and teamwork. Key things to know are Repositories (project folders), Forks (personal copies), Cloning (ge ...