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  • Object Classification Demonstration

    Demo

    Object Classification Demonstration

    This object classification demo provides a sample application for detecting, classifying, and tracking multiple objects running on CertusPro-NX FPGA.
    Object Classification Demonstration
  • Object Classification Reference Design

    Reference Design

    Object Classification Reference Design

    The Object Classification reference design shows examples on implementing machine-learning based object classification to edge devices applications.
    Object Classification Reference Design
  • Advanced CNN Accelerator IP

    IP Core

    Advanced CNN Accelerator IP

    Calculates full layers of Neural Network including convolution layer, pooling layer, batch normalization layer, and fully connected layer.
    Advanced CNN Accelerator IP
  • Human Face Identification Reference Design

    Reference Design

    Human Face Identification Reference Design

    Uses a Convolutional Neural Network in the ECP5 FPGA to detect a human face, and match to known registered faces. Can be adapted to work with any other object.
    Human Face Identification Reference Design
  • Human Presence Detection

    Reference Design

    Human Presence Detection

    Uses Lattice sensAI IP to continuously search for the presence of a human and reports results. Can be adapted to detect any other object.
    Human Presence Detection
  • Human Counting AI Demo

    Demo

    Human Counting AI Demo

    Human upper-body detection and counting demonstration utilizes Lattice’s ECP5 FPGA and a Convolutional Neural Network (CNN) acceleration engine
    Human Counting AI Demo
  • Human Face Detection AI Demo

    Demo

    Human Face Detection AI Demo

    Uses Lattice sensAI IP to detect human faces on a tiny, low-power iCE40 UltraPlus FPGA implementing AI at the edge. Adaptable to detect other objects.
    Human Face Detection AI Demo
  • Human Presence Detection AI Demo

    Demo

    Human Presence Detection AI Demo

    Uses an artificial intelligence (AI) algorithm to detect human presence with either the powerful ECP5 FPGA, or small, low-power iCE40 UltraPlus FPGA.
    Human Presence Detection AI Demo
  • Package Detection AI Demo

    Demo

    Package Detection AI Demo

    Uses Convolutional Neural Network (CNN) Accelerator IP on the ECP5 FPGA to detect packages. Output is shown via HDMI with a bounding box drawn around packages.
    Package Detection AI Demo
  • Speed Sign Detection AI Demo

    Demo

    Speed Sign Detection AI Demo

    Uses a Convolutional Neural Network in the ECP5 FPGA to detect speed limit signs and determine the indicated speed.
    Speed Sign Detection AI Demo
  • Vehicle Classification AI Demo

    Demo

    Vehicle Classification AI Demo

    Classifies vehicle types using a Convolutional Neural Network (CNN) Accelerator IP on the ECP5 FPGA. HDMI output uses color-coded bounding boxes.
    Vehicle Classification AI Demo
  • Ikva ML Accelerator IP Core

    IP Core

    Ikva ML Accelerator IP Core

    Powerful, scalable ML accelerator supporting 8-bit CNNs and 1-bit Binarized Neural Networks (BNNs), a rich software stack and computer vision models.
    Ikva ML Accelerator IP Core
  • DCA1000 Evaluation Module

    Board

    DCA1000 Evaluation Module

    The DCA1000EVM receives LVDS-format radar-sensing data and can stream over Ethernet in real-time. The board also connects to TI’s 77GHz xWR1xxx EVM.
    DCA1000 Evaluation Module
  • Machine Learning / On-device AI

    Reference Design

    Machine Learning / On-device AI

    Uses artificial intelligence (AI) to implement a human detection algorithm
    Machine Learning / On-device AI
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