Fixed-Point Convolutional Neural Network for Real-Time Video Processing in FPGA

Understand

Modern mobile neural networks with a reduced number of weights and parameters do a good job with image classification tasks, but even they may be too complex to be implemented in an FPGA for video processing tasks.

• The article proposes neural network architecture for the practical task of recognizing images from a camera, which has several advantages in terms of speed.
• This is achieved by reducing the number of weights, moving from a floating-point to a fixed-point arithmetic, and due to a number of hardware-level optimizations associated with storing weights in blocks, a shift register, and an adjustable number of convolutional blocks that work in parallel.
• The article also proposed methods for adapting the existing data set for solving a different task.