SPRY344A January   2022  – March 2023 TDA4VM , TDA4VM-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

Introduction

When consumers order a product online, automation increases efficiency throughout every step of the process, from creating raw materials, enhancing warehouse productivity and facilitating home delivery – sometimes only hours later. Continuing these remarkable advancements in automation will require better machine perception and intelligence with fewer mistakes, which can be achieved by bringing artificial intelligence (AI) to edge devices.

Creating faster, smarter and more accurate systems requires more data from more sensors, along with increasing amounts of processing power. However, more data and computing poses challenges to a system’s performance, along with its power and cost requirements. System optimization and reduced development cycle times necessitate a practical approach to designing edge AI systems.