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

5 References

  1. United Nations: Population Division
  2. A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way
  3. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012.
  4. Alfredo Canziani, Thomas Molnar, Lukasz Burzawa, Dawood Sheik, Abhishek Chaurasia, Eugenio Culurciello ,“Analysis of deep neural networks”.
  5. K. Lee, V. Rao, and W. C. Arnold, “Accelerating facebook’s infrastructure with application-specific hardware,” Facebook, 3 2019.
  6. TDAVM: Dual Arm® Cortex®-A72, C7x DSP, and deep learning, vision and multimedia accelerators
  7. Jetson Modules
  8. K26 SOM: Ideal platform for Vision AT at the edge https://www.xilinx.com/support/documentation/white_papers/wp529-som-benchmarks.pdf
  9. MLcommons benchmarking
  10. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009.
  11. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan,P. Doll´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014.
  12. TensorFlow
  13. Onnx Runtime
  14. Apache TVM
  15. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision. Springer, 2016.
  16. MLPerf, “ResNet in TensorFlow,” 2019. M. Naumov, D. Mudigere, H. M. Shi, J. Huang, N. Sundaraman
  17. Google mobilenet on embedded systems
  18. MLPerf Machine Learning Benchmark Suite - Benchmark Results
  19. TDA4VM processor starter kit for Edge AI vision systems
  20. Enabling optimized edge AI inference performance, system power and cost
  21. MLCommons Github
  22. TI's collection of optimized deep learning models
  23. Development tools for deep learning runtime
  24. TI edgeAI benchmarking repository
  25. Linux SDK for edge AI applications on TDA4VM Jacinto processors