While Deep learning has grown in leaps and bounds in just a few short years, it still requires a complex methodology reliant on trial-and-error to build certain types of models.

It is, after all, called data “science” and science is about experimentation and iteration.

That having been said, what it there were a way to speed up the iterative process?

Here’s an interesting look at the hardware options available as of 2019. They do, however, leave out FPGA.

With the increasing demand in deep learning, the demand for better as well as sophisticated hardware has also increased. Several Tier I organisations like Intel, Nvidia and Alibaba, among others, are striving hard to bridge the gap between the software and hardware. The only way to build a sophisticated deep learning model is to use better hardware.