solutions: ref-designs: modified AD-ETHERNETAPLDEVICE-SL Page

docs/learning/demo_hp_analog_meets_ai/index.rst

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+DEMO High-Performance Analog Meets AI
+===============================================================================
+
+Extracting data from high-performance, high-data-rate analog signal chains for
+AI model training and real-time inference presents significant challenges due to
+the complexity of interfaces, processing, and integration requirements. Analog
+Devices addresses these challenges by providing a comprehensive, open-source
+data extraction and integration software stack, which ensures seamless
+connectivity between advanced signal chains and high-performance compute
+platforms.
+
+Resources
+-------------------------------------------------------------------------------
+
+- HDL branch:       :git-hdl:`adrv9009_qsfp_10G <adrv9009_qsfp_10G:>`
+- Linux branch:     :git-linux:`adr9009zu11eg_100MHZ_qsfp <adr9009zu11eg_100MHZ_qsfp:>`
+- Corundum branch:  `corundum <https://github.com/ucsdsysnet/corundum.git>`__
+- PyADI-IIO branch: :git-pyadi-iio:`jupiter_modulation <jupiter_modulation:>`
+
+Block diagram
+-------------------------------------------------------------------------------
+
+.. figure:: demo_block_diagram.svg
+   :align: center
+   :width: 900
+
+Demo description
+-------------------------------------------------------------------------------
+
+This demo illustrates an AI-based multi-channel RF modulation scheme recognition
+workflow for signal intelligence applications. Four AD-JUPITER-EBZ systems are
+used to generate RF signals with different modulation schemes across a total of
+eight channels. The signals are then digitized by two ADRV9009-ZU11EG SoMs,
+which stream the raw IQ data to a host PC via 10Gb Ethernet links. The AI model,
+derived from a MathWorks reference design, is deployed on the NVIDIA GPU hosted
+in the PC. The NVIDIA Holoscan AI infrastructure manages the efficient transfer
+of data from the network interfaces into GPU memory, where the AI model is
+executed. By combining ADI’s high-performance data extraction infrastructure
+with MathWorks development tools and NVIDIA deployment frameworks, the system
+enables efficient AI application development and real-time execution for
+advanced signal intelligence tasks.
+
+.. figure:: demo_description.svg
+   :align: center
+   :width: 600
+
+System Capabilities
+-------------------------------------------------------------------------------
+
+The system demonstrates an advanced, end-to-end data extraction and AI-based
+signal processing workflow designed for high-performance signal intelligence
+applications. It combines Analog Devices’ high-speed RF hardware and data
+infrastructure with third-party AI frameworks to deliver real-time modulation
+recognition and efficient AI model development.
+
+Key capabilities include:
+
+#. High-Performance Data Extraction
+
+   * Supports real-time acquisition of high-bandwidth RF data from multi-channel
+     signal chains.
+   * Seamlessly bridges physical interfaces, FPGA-based logic, and low-level
+     software drivers to enable reliable data transfer from ADI RF front ends to
+     edge processors.
+   * Flexible connectivity options, including Ethernet, PCIe, USB, and UART,
+     allow integration with a wide range of compute platforms.
+
+#. Real-Time AI Modulation Recognition
+
+   * Demonstrates multi-channel RF modulation scheme classification using AI
+     models deployed on NVIDIA GPUs.
+   * The NVIDIA Holoscan AI infrastructure ensures efficient data movement
+     between network interfaces and GPU memory, supporting low-latency
+     inference.
+
+#. Multi-Channel & Multi-Device Synchronization
+
+   * Incorporates multiple AD-JUPITER-EBZ boards and ADRV9009-ZU11EG SoMs to
+     generate and digitize RF signals across eight channels.
+   * Provides accurate clock distribution and synchronization through
+     AD-SYNCHRONA14-EBZ, ensuring deterministic latency and coherent signal
+     processing across multiple systems.
+
+#. Seamless Data Integration Stack
+
+   * Enables flexible partitioning of data flow between edge and host compute
+     devices, improving scalability and system optimization.
+   * Utilizes an open-source ADI software stack that simplifies the setup of
+     data collection pipelines for AI model training and real-time inference.
+
+#. Integration with Industry-Standard AI Frameworks
+
+   * Compatible with MathWorks reference designs for AI model generation,
+     MATLAB-based workflows, NVIDIA Holoscan, and ROS2.
+   * Bridges data science workflows with embedded environments to enable
+     real-world dataset generation, model optimization, and deployment.
+
+#. End-to-End AI Development Ecosystem
+
+   * ADI’s AI Fusion tools within CodeFusion Studio™ enable model optimization,
+     deployment, and real-time performance analysis.
+   * Supports rapid development cycles by providing actionable insights and
+     performance metrics for system tuning.
+
+Required Hardware
+-------------------------------------------------------------------------------
+
+The following hardware components are required to set up and run the
+multi-channel RF modulation recognition demo:
+
+.. list-table::
+   :widths: 15 30 5 15
+   :header-rows: 1
+
+   * - Component
+     - Role
+     - Quantity
+     - Notes
+   * - :dokuwiki:`Jupiter SDR <resources/eval/user-guides/jupiter-sdr>`
+     - Versatile 2 x RxTx software-defined-radio platform based on ADRV9002 and
+       Xilinx Zynq UltraScale+ MPSoC. Generates RF signals with configurable
+       modulation schemes.
+     - 4
+     - Used to generate 8-channel RF input for AI recognition.
+   * - :dokuwiki:`ADRV9009-ZU11EG RF-SOM
+       <resources/eval/user-guides/adrv9009-zu11eg>`
+     - RF System-on-Module with dual ADRV9009 wideband transceivers. Performs
+       high-speed digitization and streaming of IQ data to the host.
+     - 2
+     - Provides synchronized multi-channel data acquisition.
+   * - :dokuwiki:`AD-SYNCHRONA14-EBZ
+       <resources/eval/user-guides/ad-synchrona14-ebz>`
+     - Clock synchronization and distribution board based on AD9545 and HMC7044.
+       Ensures accurate multi-channel phase alignment.
+     - 1
+     - Synchronizes all RF signal paths and data capture timing.
+   * - NVIDIA IGX Orin platform
+     - High-performance computing system with NVIDIA GPU acceleration. Runs
+       Holoscan AI infrastructure and the AI modulation recognition model.
+     - 1
+     - Requires 10Gb Ethernet connectivity.
+   * - SMA Cables
+     - RF connection between the SDR transmit and receive channels.
+     - 8
+     - High-quality coaxial cables recommended for minimal signal loss.
+   * - 100G QSFP28 Active Optical Cable
+     - Provides high-speed data connection between the RF-SOM and the host
+       compute platform.
+     - 1
+     - Supports low-latency, high-bandwidth Ethernet link.
+   * - Network switch with at least 4 PoE ports
+     - Provides Ethernet connectivity and power delivery to connected devices.
+     - 1
+     - Use a managed switch compatible with 10GbE interfaces.
+
+SD Card Configuration
+-------------------------------------------------------------------------------
+
+- For the Jupiter SDR platform, the boot files are generated using the Using
+  Kuiper Image:
+
+  :external+adi-kuiper-gen:ref:`Writing the Image to an SD Card
+  <use-kuiper-image>`
+
+- For the ADRV9009-ZU11EG, begin by checking out the HDL branch, then navigate
+  to the **adrv2crr_fmc** directory.
+
+Run the following command to enable Corundum support and build the design:
+**make CORUNDUM=1** Once the build process is complete, generate the necessary
+boot files: boot.bin, device tree, and uImage by following the steps:
+
+- BOOT.BIN: :external+hdl:ref:`Build the boot image BOOT.BIN <build_boot_bin>`
+- Devicetree: :dokuwiki:`Building the Zynq Linux kernel and devicetrees from
+  source <resources/tools-software/linux-build/generic/zynq?s%5b%5d=devicetree>`
+
+Capture in Data Using Scopy2.0
+-------------------------------------------------------------------------------
+
+Captured RF Signal in Time Domain
+
+.. figure:: capture_time.jpg
+   :align: center
+   :width: 900
+
+Captured RF Signal in Frequency Domain
+
+.. figure:: capture_frequency.jpg
+   :align: center
+   :width: 900
+
+AI Modulation Detection Applications
+-------------------------------------------------------------------------------
+
+.. toctree::
+   :caption: The following applications are available:
+   :maxdepth: 1
+
+   software/index