FPGAs could replace GPUs in many deep learning applications

The renewed interest in artificial intelligence in the past decade has been a boon for the graphics cards industry. Companies like Nvidia and AMD have seen a huge boost to their stock prices as their GPUs have proven to be very efficient for training and running deep learning models. Nvidia, in fact, has even pivoted from a pure GPU and gaming company to a provider of cloud GPU services and a competent AI research lab. But GPUs also have inherent flaws that pose challenges in putting them to use in AI applications, according to Ludovic Larzul, CEO and co-founder of Mipsology, a company that specializes in machine learning software. The solution, Larzul says, are field programmable gate arrays (FPGA), an area where his company specializes. FPGA is a type of processor that can be customized after manufacturing, which makes it more efficient than generic processors.