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Add TFLite Edge TPU Docs Integrations Page (#8900)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/integrations/openvino.md
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@ -8,7 +8,7 @@ keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, expo
<img width="1024" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/2b181f68-aa91-4514-ba09-497cc3c83b00" alt="OpenVINO Ecosystem">
In this guide, we cover exporting YOLOv8 models to the [OpenVINO](https://docs.openvino.ai/) format, which can provide up to 3x [CPU](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_CPU.html) speedup as well as accelerating on other Intel hardware ([iGPU](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_GPU.html), [dGPU](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_GPU.html), [VPU](https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_VPU.html), etc.).
In this guide, we cover exporting YOLOv8 models to the [OpenVINO](https://docs.openvino.ai/) format, which can provide up to 3x [CPU](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.html) speedup, as well as accelerating YOLO inference on Intel [GPU](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) and [NPU](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.html) hardware.
OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit, is a comprehensive toolkit for optimizing and deploying AI inference models. Even though the name contains Visual, OpenVINO also supports various additional tasks including language, audio, time series, etc.