The most popular and comprehensive Open Source ECM platform
Let us know more
Artificial Intelligence Chips: GPU Competitors
It wasn’t meant to be, but today’s AI industry has a strong focus on the GPU chip. GPUs were originally designed for high-end compute-intense processing for graphics, but they have been increasingly used in AI, especially for deep learning and neural networks.
GPUs are able to process thousands of tasks simultaneously in hundreds of processor cores, and do it significantly with less power that standard CPUs.
Nvidia is the leading producer of GPUs and has seen enormous success in the sales of their chips. But GPUs are likely to be supplanted soon by other chip architectures that were designed specifically for handling AI algorithms. And those newer architectures are likely to begin taking market share from the GPU market.
Nigel Toon, CEO of Graphcore, said that “a GPU is a pretty good solution if all you are doing is basic feed-forward convolutional neural networks, but as the networks become more complex, people need a new solution — that’s why they’re playing with ASICs and FPGAs. All the innovators we spoke to said using GPUs is holding them back from new innovations. If you look at the types of models that people are working on, they are primarily working on forms of convolutional neural networks because recurrent neural networks and other kinds of structures, [such as] reinforcement learning, don’t map well to GPUs. Areas of research are being held back because there isn’t a good enough hardware platform.”
