tinyML Talks Pakistan: FFConv: An FPGA-based Accelerator for Fast Convolution Layers in...

FFConv: An FPGA-based Accelerator for Fast Convolution Layers in Convolutional Neural Network Muhammad Adeel Pasha Associate professor Department of Electrical Engineering Lahore University of Management Sciences Image classification is known to be one of the most challenging problems in the domain of computer vision. Significant research is being done on developing systems and algorithms improving accuracy, performance, area, and power consumption for related problems. Convolutional Neural Networks (CNNs) have been shown to give outstanding accuracies for problems such as image classification, object detection and semantic segmentation. While CNNs are pioneering the development of high accuracy systems, their excessive computational complexity presents a barrier for a more permeated deployment. Although Graphical Processing Units (GPUs), due to their massively parallel architecture, have been shown to give performance orders of magnitude better than general-purpose processors, the fo