Hand-Written Number Classification by Hardware Neural Network

This study explores the application of hardware neural networks for handwritten digit recognition, focusing on the implementation of the perceptron model. Handwritten digit recognition is a key technology in fields such as automatic postal sorting and bank cheque processing. In this work, a hardware neural network based on Frank Rosenblatt’s perceptron model is employed to efficiently process and classify handwritten digits. The system utilizes Verilog and VLSI technology to handle inputs in a parallel and pipelined fashion, greatly reducing computation time compared to traditional serial methods. Specifically, the designed system can recognize all ten digits in just 392 clock cycles, demonstrating significant improvements in efficiency while maintaining accuracy. Parallel processing and pipelining techniques are used to optimize performance, and experimental results show accurate identification of the test images. This research highlights the potential of hardware-based neural networks in applications such as embedded systems and IoT, emphasizing the role of hardware acceleration in advancing artificial intelligence technologies.