SPRY344A January   2022  – March 2023 AM67 , AM67A , AM68 , AM68A , AM69 , AM69A , TDA4AEN-Q1 , TDA4AH-Q1 , TDA4AL-Q1 , TDA4AP-Q1 , TDA4APE-Q1 , TDA4VE-Q1 , TDA4VEN-Q1 , TDA4VH-Q1 , TDA4VL-Q1 , TDA4VM , TDA4VM-Q1 , TDA4VP-Q1 , TDA4VPE-Q1

 

  1.   At a glance
  2.   Authors
  3.   Introduction
  4.   Defining AI at the edge
  5.   What is an efficient edge AI system?
    1.     Selecting an SoC architecture
    2.     Programmable core types and accelerators
  6.   Designing edge AI systems with TI vision processors
    1.     Deep learning accelerator
    2.     Imaging and computer vision hardware accelerators
    3.     Smart internal bus and memory architecture
    4.     Optimized system BOM
    5.     Easy-to-use software development environment
  7.   Conclusion

Conclusion

The adoption of a heterogeneous architecture in applications is growing. TI's vision AI processors, with accelerated deep learning, vision and video processing, purpose-built system integration, and advanced component integration, enables commercially viable edge AI systems optimized for performance, power, size, weight and system costs. TI 's edge AI software development environment is built around open-source, industry-standard APIs, with automatic acceleration to hardware accelerators that enable faster edge AI application development.

AI is a rapidly evolving technology, fostering innovations in all dimensions of edge AI applications. It is pushing the boundaries of applications requiring higher computation needs. When enabled at lower power and lower system costs through the implementation of an embedded processor, edge AI can open up a whole new world of possibilities with embedded applications.