Management AI: GPU and FPGA, Why They Are Important for Artificial Intelligence

In business software, the computer chip has been forgotten. Robotics has been more tightly tied to individual hardware devices, so manufacturing applications are still a bit more focused on hardware. The current state of Artificial Intelligence (AI), in general, and Deep Learning (DL) in specific, is more tightly tying hardware to software than at any time in computers since the 1970s. While my last few "management AI" articles were about overfit and bias, two key risks in a machine learning (ML) system. This column digs deeper to address the question many managers, especially business line managers, might have about the hardware acronyms constantly mentioned in the ML ecosystem: Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). It helps to understand that the GPU is valuable because it accelerates the tensor (math) processing necessary for deep learning applications.