Today, artificial intelligence models are generally run on GPU-based hardware and platforms. Running artificial intelligence models on GPU has some advantages as well as disadvantages. For example, in embedded system projects, GPU systems have disadvantages such as taking up large space, costing and consuming a lot of power.
So how can we run artificial intelligence models on the CPU? We can do this with the OpenVINO (Open Visual Inference & Neural Network Optimization) architecture, which is the most popular among current technologies.
OpenVINO is a software platform developed by Intel that allows developers to build and deploy computer vision and deep learning applications on a variety of hardware devices. This architecture works only on INTEL processors. It provides a set of tools and libraries that can be used to optimize and accelerate the performance of machine learning models on various hardware platforms, including CPUs, GPUs, and VPUs (Vision Processing Units). OpenVINO is particularly useful for building and deploying computer vision and deep learning applications on edge devices such as cameras, drones and IoT (Internet of Things) devices where low power consumption and real-time performance are critical. It can be used in a wide variety of applications, including object detection, image classification, and face recognition. In this way, it is a serious advantage to get fast performance with low power consumption and to do this with less cost
- Surveillance and Security Systems
- Industrial automation
- Retail Analytics
- Agriculture and Environmental Monitoring
- Autonomous Vehicles
It is designed to work with a variety of machine learning frameworks, including OpenVINO, TensorFlow, PyTorch, YOLO, and Caffe, and supports a variety of programming languages, including Python, C++, and Java. It is a powerful tool for building and deploying efficient and scalable computer vision and deep learning applications on edge devices.
Although the OpenVINO architecture seems to be the most popular and unrivaled at the moment, it should be said that it will create serious competition with the entry of the Deepsparse architecture into the market. Briefly: DeepSparse is a type of neural network architecture that aims to increase the efficiency and effectiveness of deep learning models.
We will explain this in more detail in our next articles.
