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

3.3.1 Benchmarking against GPU-based architectures

Figure 3-1 plots the above TDA4VM results against a GPU based SoC numbers from the MLcommons page [19] for the single-stream mode use case. As we discussed before, FPS/TOPS is a better indicator of energy efficiency.

GUID-FE0877D9-3D15-45E8-93AD-55E2E485C5D3-low.gif Figure 3-1 GPU based SoC vs TDA4VM: FPS/TOPS comparison

We can see from the comparison that TDA4VM is up to 60% better in terms of FPS/TOPS efficiency. What this means is that 60% less TOPS are needed to run equivalent deep learning functions.