SPRACZ2 August   2022 TDA4VM , TDA4VM-Q1

ADVANCE INFORMATION  

  1.   Abstract
  2. 1Introduction
    1. 1.1 Vision Analytics
    2. 1.2 End Equipments
    3. 1.3 Deep learning: State-of-the-art
  3. 2Embedded edge AI system: Design considerations
    1. 2.1 Processors for edge AI: Technology landscape
    2. 2.2 Edge AI with TI: Energy-efficient and Practical AI
      1. 2.2.1 TDA4VM processor architecture
        1. 2.2.1.1 Development platform
    3. 2.3 Software programming
  4. 3Industry standard performance and power benchmarking
    1. 3.1 MLPerf models
    2. 3.2 Performance and efficiency benchmarking
    3. 3.3 Comparison against other SoC Architectures
      1. 3.3.1 Benchmarking against GPU-based architectures
      2. 3.3.2 Benchmarking against FPGA based SoCs
      3. 3.3.3 Summary of competitive benchmarking
  5. 4Conclusion
  6.   Revision History
  7. 5References

1.2 End Equipments

The AI-enabled vision market is an exciting area with rapid growth expected in the next few years. Vision based analytics have broad use cases across many different markets and end equipment as shown in Figure 1-2.

GUID-3613AB79-2039-43A4-A5A6-355A83E67DDA-low.jpg Figure 1-2 Example Vision AI applications

For example, a robot in factory or warehouse setting or a last-mile robot can use vision-based analytics to do the below functions.

  1. Obstacle detection
  2. Pose estimation

A surveillance camera can be smarter with edgeAI functionality by analyzing the objects to do extended functions such as:

  1. Automatic object detection
  2. Intrusion and hazard detection
  3. Safety monitoring

A smart shopping cart can use vision-based analytics to completely automate shopping experience for better efficiency and lower costs to consumers by adding functions such as:

  1. Automatic checkout
  2. Real-time pricing and nutrition information display
  3. Retail Analytics