Design of Optimized CNN for Image Processing using Verilog

This paper presents the implementation of Optimized CNN for Image Processing using Verilog. The main emphasis lies in harnessing the capabilities of CNNs (Convolutional Neural Networks) for image processing tasks, such as image compression and accentuating descriptor points within an image. The fundamental component of the suggested approach is designing and developing an architecture capable of successfully carrying out CNN-based image processing activities. Verilog is used as the hardware description language to model and simulate the hardware components. Synthesis tools are employed to convert the Verilog code into a gate-level representation suitable for implementation on FPGA or ASIC platforms. The Verilog- based optimized CNN design enable faster and efficient execution of image processing algorithms compared to traditional software implementations. Real-time performance can be accomplished by offloading the computing task to specialized hardware, making it appropriate for applications with strict latency constraints. The image size is decreased by the image compression method used in this work. while preserving important visual information. Verilog allows the effective implementation of image scaling algorithms in hardware, enabling CNNs like VGG to operate effectively on devices with the limited resources. In this article, we will explore the integration of VGG model with Verilog to achieve image size reduction and improve CNN’s performance based applications in resource constrained environments.