With the advances of deep learning models, more applications utilize multiple camera inputs which provide tremendous benefits which are not possible with a single camera. Some of the applications which can benefit from multi-camera inputs include surround view, cabin recorder and camera mirror system, medical imaging, security surveillance and many more.
AM62A is equipped with a deep learning accelerator (C7x-MMA) with up to 2 TOPS which is capable of running various types of deep learning models for classification, object detection, or semantic segmentation simultaneously. This demo presents a reference design of running multiple-camera applications on AM62A EVM, using the Arducam V3Link Camera Solution Kit (https://docs.arducam.com/V3Link-Camera-Solution/V3Link-Camera-Solution-on-TI-Platform/Introduction/) to connect up to 4 CSI-2 cameras to AM62A and running deep learning models for all 4 cameras. This V3Link kit works with both FPD-Link and V3-Link based cameras.
The he four cameras AI demo operated at 30 FPS each with total 120 FPS for the four cameras. Even with this high rate, only 86% of the deep learning accelerator C7x-MMA is utilized. This rate is limited by the performance of the cameras used in the testing (imx219) which does not exceed 30 FPS. In addition, the deep learning accelerator is clocked at 850 MHz instead of 1000 MHz. This means that the in these experiments, the C7x-MMA is running at about only 85% of its expected performance. This extra bandwidth of the deep learning accelerator can be utilized for bigger deep learning models or higher frame rate depending on the targeted application.
Following are the steps to run the demo:
Purpose | Link |
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Arducam V3Link Camera Solution Kit | https://docs.arducam.com/V3Link-Camera-Solution/V3Link-Camera-Solution-on-TI-Platform/Introduction/ |
Setup the V3Link Kit following the | https://dev.ti.com/tirex/explore/node?node=A__AQniYj7pI2aoPAFMxWtKDQ__am62ax-devtools__FUz-xrs__LATEST |
Application note: Detailed reference design and performance analysis of using Multiple-Camera Applications on AM6x processors. | https://www.ti.com/lit/an/spradh2/spradh2.pdf |
Please find the following resources related to the AM62A and TI Edge AI.
Purpose | Link |
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AM62A product page | https://www.ti.com/product/AM62A7/ |
AM62A Starter Kit EVM | https://www.ti.com/tool/SK-AM62A-LP/ |
AM62A EVM Quick Start Guide | https://dev.ti.com/tirex/explore/node?node=A__AQniYj7pI2aoPAFMxWtKDQ__am62ax-devtools__FUz-xrs__LATEST/ |
TI Edge AI Studio: Model Analyzer | https://dev.ti.com/edgeaisession/ |
TI Edge AI Studio: Model Composer | https://dev.ti.com/modelcomposer/ |
TI Edge AI Academy | https://dev.ti.com/tirex/explore/node?node=A__AN7hqv4wA0hzx.vdB9lTEw__EDGEAI-ACADEMY__ZKnFr2N__LATEST/ |
Top level github page for Edge AI | https://github.com/TexasInstruments/edgeai/ |
AM62A Datasheet (superset device) | https://www.ti.com/lit/ds/sprsp77/sprsp77.pdf |
AM62A Academy (Basic Linux Training/bringup) | https://dev.ti.com/tirex/explore/node?node=A__AB.GCF6kV.FoXARl2aj.wg__AM62A-ACADEMY__WeZ9SsL__LATEST |