The startup making deep learning possible without specialized hardware

GPUs became the hardware of choice for deep learning largely by coincidence. The chips were initially designed to quickly render graphics in applications such as video games. Unlike CPUs, which have four to eight complex cores for doing a variety of computation, GPUs have hundreds of simple cores that can perform only specific operations--but the cores can tackle their operations at the same time rather than one after another, shrinking the time it takes to complete an intensive computation. It didn't take long for the AI research community to realize that this massive parallelization also makes GPUs great for deep learning. Like graphics-rendering, deep learning involves simple mathematical calculations performed hundreds of thousands of times.