নিয়মিত কার্যক্রম

The intersection of artificial intelligence and embedded systems is opening up new possibilities for smart devices. Using platforms like Arduino and ESP32, developers can now deploy lightweight machine learning models directly onto hardware for real-time data processing. This concept, known as TinyML, allows devices to perform tasks like voice recognition, gesture detection, and environmental monitoring without relying on constant cloud connectivity. The challenge lies in optimizing models to fit within the limited memory and processing power of microcontrollers. Tools like TensorFlow Lite for Microcontrollers have become instrumental in this process, enabling the conversion of complex neural networks into efficient code. Additionally, implementing low-power modes ensures that battery-operated AI devices can function for extended periods. As the hardware becomes more capable and the software tools more accessible, we are seeing a surge in home automation and industrial monitoring solutions that utilize edge AI. This trend empowers developers to build privacy-focused applications where data never leaves the device, providing both security and low-latency performance for the end user.