First of all, what is actually AI hardware and how it differs from the general hardware we are used to. Essentially, when we talk about AI hardware, we refer to some type of AI accelerators — a class of microprocessors, or microchips, designed to enable faster processing of AI applications, especially in machine learning, neural networks and computer vision. They are usually designed as manycore and focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability.
The idea behind AI accelerators is that a large part of AI tasks can be massively parallel. With a general purpose GPU (GPGPU), for example, a graphics card can be used in massively parallel computing implementations, where they deliver up to 10 times the performance of CPUs.
The second pillar of AI accelerators design is focused on multicore implementation. Think of a GPU that can accelerate such tasks using many simple cores that are normally used to deliver pixels to a screen. These cores are designed for simpler arithmetic functions common to AI, where the number of simple functions grows so high that traditional computing approaches fail. With purpose-designed application-specific integrated circuits (ASICs), efficiency can be even greater than that achieved with GPGPU, which can benefit edge AI tasks.
Generally speaking, a purpose-made accelerator delivers greater performance, more features and greater power efficiency to facilitate a given task.