Getting Started with the FRWY-LS1046A | NXP Semiconductors

Getting Started with the FRWY-LS1046A

Last Modified: May 9, 2019Supports LS1046A Freeway

1. Plug It In

1.1 Out of the Box

The FRWY-LS1046A is a high-performance, low-cost edge computing, evaluation, and development platform that supports the LS1046A architecture quad-core 64-bit processor and is capable of supporting more than 40,000 CoreMark performance. The platform can enable a wide range of high-compute applications including AI/ML, industrial gateway, smart building gateway, wireless gateway, and many more.

1.1 Out of the Box

The FRWY-LS1046A comes in two development platform configurations - the FRWY-LS1046A-AC and the FRWY-LS1046A-TP, which adds a Google TPU module for accelerated inferencing for AI/ML applications. Unbox your new hardware kit and review the contents inside the box.

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Figure 1. FRWY-LS1046A board back and front.

FRWY-LS1046A-TP FRWY-LS1046A-AC
FRWY-LS1046A board hardware assembly with enclosure. FRWY-LS1046A board hardware assembly with enclosure.
M.2 cards installed:
  • Google Coral TPU module
  • SparkLAN dual-band Wi-Fi + Bluetooth NGFF card
M.2 card installed:
  • SparkLAN dual-band Wi-Fi + Bluetooth NGFF card
12 V, 5 A DC power adapter 12 V, 5 A DC power adapter
Universal AC input adapter1 Universal AC input adapter1
USB Type A to micro-B cable USB Type A to micro-B cable
32 GB TF (micro-SD) card, programmed for FRWY-LS1046A-TP 32 GB TF (micro-SD) card, programmed for FRWY-LS1046A-AC
Shunt jumpers (5) Shunt jumpers (10)
Antennas (2) Antennas (2)
Quick reference card2 Quick reference card2

1.2 Getting Started with FRWY

Watch the video below or follow the steps to set up and boot your development kit. Note that the FRWY-LS1046A-PA development platform referenced in the video is no longer in production.

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      1. Insert the SD-Card in the SD-socket. This is required for the FRWY-LS1046A system to boot.
      2. Mount the provided Wi-Fi antennas into the SMA plugs on the FRWY-LS1046A system.
      3. Plug in the USB webcam to either one of the two USB ports on FRWY-LS1046A board.
      4. Power on the board by plugging in the power supply to the barrel connector with the included 12V power supply.
      5. Wait for 2 minutes for the board to boot up and broadcast the Wi-Fi SSID, which comes up automatically.
      6. Connect cell phone, tablet, personal computer etc. to the SSID “FRWY-LS1046A-[MAC ADDRESS]”, with password "frwyls1046a". The [MAC ADDRESS] can be found as the last four digits of the first Ethernet MAC address labeled on the sticker at the bottom surface of board.
      7. Open a web browser on your device and type in 192.168.99.1 to demonstrate the use cases for Performance Benchmarking, Data Analytics, Machine Learning and Demo Videos. Make sure you enable pop-ups in your browser, or you will not be able to view the results of these demonstrations.

      Note:

      • If you are using a cell phone, please turn off the mobile data as the above URL is local to the board.
      • Ethernet port “1G PORT2” can also be used to access GUI at the same URL above, in case Wi-Fi is not available on board.
      • You will need a USB webcam and the PN7120 NFC MikroBUS(TM) add-on board  to run the onboard demos.

      2. Get Software

      2.1 USB-to-UART bridge virtual COM port (VCP) drive

      To add a serial connection between the host computer and board, download and install the appropriate driver (based on Windows version) onto the host computer from the following link.

      2.2 Tera Term (serial terminal emulator)

      To configure a serial connection and see console prints, download and install Tera Term on the host computer from the following link:

      2.3 CodeWarrior® for NET Applications

      To debug and control the board, order CodeWarrior Development Studio from the following link:

      2.4 Layerscape SDK for FRWY-LS1046A

      Review the LSDK packages available for the LS1046A Platform.

      2.5 FRWY-LS1046A Demo Patches for LSDK

      Demo patches for the latest version of the LSDK are available at the following link. If you want to compile LSDK 1909 images with LS1046A-FRWY demos, use the demo patches at the link below and follow this application note.

      3. Build, Run

      3.1 FRWY-LS1046A System GUI Introduction

      This video walks you through the Graphical User Interface (GUI) and introduces the various demos that are available on the FRWY-LS1046A. Refer to application note AN12737 Demonstrating FRWY-LS1046A Board Capabilities for additional specifics about each demo.

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        The demos that are pre-loaded on the FRWY-LS1046A are described below. Demos labeled as TPU are only available on the FRWY-LS1046A-TP.

        • Face Recognition Demo(CPU)

          Face Recognition Demo using NCNN and MobileFaceNet shows we can train any number of people and store names in database for future use.
          Multi face detection at single time.
        • People Counting Demo(CPU)

          A pre-trained coco model is used for people counting.
          The model can detect up to 80 objects.
          ML/AI capabilities powered by TensorFlow and OpenCV which provide ease of use and smooth deployment and development options to users.
        • Industrial Safety and Security Demo(CPU)

          Industrial Safety demo using TensorFlow shows Machine learning and AI capabilities of FRWY-LS1046A.
          Trained model for almost any object can be added using this method.
          ML/AI capabilities powered by TensorFlow and OpenCV which provide ease of use and smooth deployment and development options to users.
        • Industrial Safety and Security Demo(TPU)

          Industrial Safety demo using TFLite shows Machine learning and AI capabilities of FRWY-LS1046A.
          With the help of TPU we can process approx 30 frame per second and detection speed is very good.
          In this demo, we are showing the count of google detection on UI.
        • Pose Detection Demo(TPU)

          Pose Detection Demo is use for the activity and position of user.
          This demo is running on TPU so we can process approx 30 frames per second.
          ML/AI capabilities powered by TFLite and OpenCV which provide ease of use and smooth deployment and development options to users.