A proposed method for computing convolution neural network algorithms - Hussein K. Khleaf; Ali K. Nahar; Nuha H. Abdulghafoor; Ansam Subhi Jabbar (المجلة الدولية للحاسبات والمعلوماتية, 2026)
One kind of deep neural network that uses low-level input, such shapes and lines, to find more nuanced patterns is called a convolutional neural network (CNN). CNNs compare subsets of data using a kernel or filter through a variety of convolution processes. Training deep convolutional neural networks on large datasets requires days of GPU processing. Very low latency is necessary for self-driving automobiles to identify pedestrians. This study examines a suggested quick way to calculate the operations needed by convolutional neural network algorithms. In these situations, the speed at which convolution neural networks compute determines how well they perform. MATLAB is used to build this algorithm, and a single graphical user interface (GUI) window is used for all operations. When the approach is used instead of MATLAB’s built-in functions, processing time is decreased. It saves 70% of the processing time when compared to the built-in features. This technique works on the widely recognized fast table method (FTM) principle.