optimizing AI systems for limited memory environments is an important consideration. Here are a few examples of how AI models with limited memory requirements are used:

1. Edge Devices and IoT: Many Internet of Things (IoT) devices and edge devices have limited computational power and memory. AI models are often deployed on these devices for tasks like image recognition, speech processing, and anomaly detection. These models need to be compact to run efficiently on these devices.
2. Medical Imaging: AI models that analyze medical images, such as X-rays and MRIs, are often deployed on devices with limited resources within hospitals or even directly on medical equipment. These models help doctors diagnose conditions and make treatment decisions.
3. Autonomous Vehicles: AI is used in autonomous vehicles for various tasks, including object detection, lane detection, and decision-making. These systems need to operate in real-time and must be designed to fit within the limited computational resources of the vehicle.
4. Smartphones: AI-driven applications on smartphones, like virtual assistants and language translation, need to be memory-efficient to ensure smooth performance and minimize battery drain.
5. Remote Sensing: AI models are employed for analyzing satellite imagery and aerial photographs in applications such as environmental monitoring, agriculture, and disaster response. These models must be optimized for efficient memory usage to process large datasets.
6. Retail and Inventory Management: AI models that help with inventory management and demand forecasting are often deployed in retail settings. These models need to work within the constraints of point-of-sale systems and handheld devices.
7. Language Translation: AI-powered language translation apps on wearable devices or small gadgets require memory-efficient models to provide real-time translations without overloading the device’s resources.