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
The expansion of automation is increasing from the factory floor to the front door.

At a glance

At a glance This white paper explains the requirements for building an efficient edge artificial intelligence (AI) system and how the vision AI processors can help optimize performance due to a heterogenous architecture and scalable AI performance.

1 Defining AI at the edge
Defining artificial intelligence at the edge. Many different kinds of systems can benefit from edge AI processing.
2 What is an efficient edge AI system?
What is a practical edge AI system? Consider which architecture and cores will best complete the tasks required of a system.
3 Designing edge AI systems with TI vision processors
Designing edge AI systems with vision AI processors like the TDA4 and AM6xA systems on chip (SoCs). These SoCs are designed to deliver scalable throughput and computing performance at low power and with lower system BOM costs.