SPRADB8 may 2023 AM62A3 , AM62A7
A reference application was developed for the AM62A to showcase its capabilities for an automated checkout system using an object detection neural network. A custom model was trained to recognize a dozen different food items, and a Linux Python3 application was written around this model using TI’s gstreamer plugins to leverage hardware acceleration where possible. Application source code [2] and in-depth description on how the demo works are available on Github. For further guidance on building an application like this, see the associated application note [3]. The block diagram in Figure 4-1 depicts the application flow within gstreamer and how various plugins execute on remote cores within the SoC.
This document analyzes this application and uses the core load both to guide selection of a suitable AM62A variant as well as provide a power usage estimate using the Power Estimation Tool [4]. This analysis can be followed for other applications designed and benchmarked on the Starter Kit EVM, which uses the superset variant AM62A74 (2 TOPS acceleration, 4 Arm® Cortex A53 cores).