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

2.3 Software programming

TI’s Edge AI starter kit come with a comprehensive edge AI software architecture that enables developers to do application development completely in Python or C++ language. There is no need to learn any special language to take advantage of the performance and energy efficiency of the deep learning accelerator with the TDA4x SoC processor.

Figure 2-6 below shows the comprehensive software offering from TI for the edgeAI applications. With strong and robust Linux foundation, developers can program the device with popular open-source frameworks such as, Tensorflow lite, ONNX, and TVM. Figure 2-6 shows the software architecture used for edge AI application development.

GUID-84B1C332-1C5A-44B8-A698-0D4DDF9EFEBF-low.gif Figure 2-6 Easy to use complete software for easy AI application development