Increase the capabilities of devices for artificial intelligence

The CPU still performs well for business systems or in applications where the need for general programming flexibility outweighs pure processing power. However, GPUs are now the standard for different types of data science, machine learning, artificial intelligence, and deep learning needs. Of course, everyone is constantly looking for the next big thing in the development environment. Both CPUs and GPUs are production level processors. In the future, you may see one of two types of processors used in place of these criteria:

  • Application Specific Integrated Circuits (ASICs): Unlike generic processors, the vendor creates an ASIC for a specific purpose. The ASIC solution provides extremely fast performance using very little power, but lacks flexibility. An example of an ASIC solution is Google’s Tensor Processing Unit (TPU), which is used for speech processing.


  • Field Programmable Gate Arrays (FPGAs): As with ASICs, the vendor generally formulates an FPGA for a specific purpose. However, unlike an ASIC, you can program an FPGA to change its basic functions. An example of an FPGA solution is Microsoft’s Brainwave, which is used for deep learning projects.

The battle between ASICs and FPGAs is poised to escalate, with AI developers emerging as the winners. For now, Microsoft and FPGAs seem to have taken the lead. The point is that technology is seamless, and you should expect to see new developments.
Vendors are also working on completely new processing types, which may or may not actually work as expected. For example, Graphcore runs on an Information Processing Unit (IPU). You have to take the news of these new processors very carefully given the hype that has surrounded the industry in the past. When you see real apps from big companies like Google and Microsoft, you can start to feel more certain about the future of the technology in question.