Published Paper Details:

In many applications such as artificial intelligence, computer vision, multitasking, image recognition and digital signal processing, the connections are simple arithmetic operations. The use of heavy equipment leads to large power consumption, which is very difficult, especially in weak environments such as mobile devices. We offer a unique way to solve this problem by reducing energy consumption by controlling accuracy. Our solutions are based on the concept of flexible switching, which can reduce energy consumption and increase performance at a certain cost. We present a new model that enables adaptive changes in accuracy based on application errors. The basis of our idea is to create a 4-2 compressor with extraordinary precision, which will work as the basis of a dynamic precision control system. We also include powerful error compensation to improve the accuracy of the addition process. In addition, by integrating parallel assertions into a convolutional neural network (CNN), we demonstrate the simplicity and flexibility of the real-time control system. As this integration shows, the multiplier is an excellent solution for deep learning applications, demonstrating the ability of each layer to meet different and energy needs. As a result, our scheme will be a good idea to reduce electricity consumption. Enabled by groundbreaking dynamic accuracy control, on-the-fly adjustments intelligently reduce power consumption while maintaining control's received learning levels. Our company's design offers an attractive next-generation energy efficiency solution because it reduces energy consumption and can be optimized to meet different needs of the application.