# A Novel FPGA-based CNN Hardware Accelerator: Optimization for Convolutional Layers using Karatsuba Ofman Multiplier Source: [A Novel FPGA-based CNN Hardware Accelerator: Optimization for Convolutional Layers using Karatsuba Ofman Multiplier]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:54:27.044Z Authors: Amit Sarkar Published: 2024-12-29T08:09:35.000Z ## Abstract A new architecture of CNN hardware accelerator is presented. Convolutional Neural Networks (CNNs) are a subclass of neural networks that have demonstrated outstanding performance in a variety of computer vision applications, including object detection, image classification, and many more.Convolution, a mathematical operation that consists of multiplying, shifting and adding a set of input values by a set of learnable parameters known as filters or kernels, which is the fundamental component of a CNN.The Karatsuba Ofman multiplier is known for its ability to perform high-speed multiplication with less hardware resources compared to traditional multipliers. This article examines the usage of the Karatsuba Ofman Multiplier method on FPGA in the prominent CNN designs AlexNet, VGG16, and VGG19. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()