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

3.3.2 Benchmarking against FPGA based SoCs

The Xilinx Kria K26, is based on the Zynq® UltraScale+™ MPSoC architecture with various deep learning processing unit (DPU) configurations. Xilinx has an application note [8] publishing the FPS/Watt numbers for the MLPerf models chosen in our study. However, these results are not yet published on Mlperf website. So, just comparing the 1.2 TOPS vs 8 TOPS of performance that TDA4VM platform brings, developers will have more than six times the performance boost to design more sophisticated AI vision tasks.