Enter microcontrollers of the future – the simplest, very small computers. They run on batteries for months or years and control the functions of the systems embedded in our home appliances and other electronics.
Our latest breakthrough in AI training, detailed in a paper presented at this year’s NeurIPS conference, is expected to dramatically cut AI training time and cost. So considerably in fact that it could help completely erase the blurry border between cloud and edge — offering a key technological upgrade for hybrid cloud infrastructures.
AI’s unprecedented demand for data, power and system resources poses the greatest challenge to realizing this optimistic vision of the future. To meet that demand, we’re developing a new class of inherently energy-efficient AI hardware accelerators that will increase compute power by orders of magnitude, in hybrid cloud environments, without the demand for increased energy.