FPGA-Accelerated Convolutional Neural Network

In recent years, FPGA has become an attractive
solution to accelerate CNN classification for its flexibility, short
time-to-market, and energy efficiency. The real-time evaluation
of a CNN for image classification on a live video stream can
require billions or trillions of operations per second. To come
with a competitive re-configurable implementation satisfying both
development time and flexibility, we propose using as a base a
re-configurable Architecture composed by a set of image and
video processing blocks. The whole architecture can be configured
on-the-fly based on the image characteristics thus supporting
variable image resolutions for each layer of the CNN.