ASIC stands for "application specific integrated circuit" and the name conveys a special purpose design of an integrated circuit, like for example bit coin mining.
But that's just terminology/convention. One could argue a GPU is an ASIC, and a CPU is an ASIC. The only thing to argue is how specific does an application have to be to call it an ASIC instead of some other made up name.
The prime differentiators are the process, which is the term used for the many steps of fabrication of an IC (processes are often referred to as nodes, distinguished by the smallest feature size of a transistor they create), and the ability of the design engineers to create an optimal design, in terms of boolean logic and semiconductor physics.
Given the right team of engineers, and a top notch foundry, and a great deal of experience in the problem domain (machine learning in this case), a custom IC could very likely trounce a GPU.
GPUs were designed for the domain of graphics processing, which happens to have some commonality with the processing in machine learning. But, at least until recently, GPUs weren't focused on machine learning. Just graphics.
Now the GPU vendors are trying to leverage their knowledge of graphics processing and building of graphics processors to create machine learning processors, but the thing they are leveraging could also be what handcuffs them. Which gives opportunity for a company like Google to do a fresh take on the problem domain without the baggage of the knowledge of graphics processing.
But that's just terminology/convention. One could argue a GPU is an ASIC, and a CPU is an ASIC. The only thing to argue is how specific does an application have to be to call it an ASIC instead of some other made up name.
The prime differentiators are the process, which is the term used for the many steps of fabrication of an IC (processes are often referred to as nodes, distinguished by the smallest feature size of a transistor they create), and the ability of the design engineers to create an optimal design, in terms of boolean logic and semiconductor physics.
Given the right team of engineers, and a top notch foundry, and a great deal of experience in the problem domain (machine learning in this case), a custom IC could very likely trounce a GPU.
GPUs were designed for the domain of graphics processing, which happens to have some commonality with the processing in machine learning. But, at least until recently, GPUs weren't focused on machine learning. Just graphics.
Now the GPU vendors are trying to leverage their knowledge of graphics processing and building of graphics processors to create machine learning processors, but the thing they are leveraging could also be what handcuffs them. Which gives opportunity for a company like Google to do a fresh take on the problem domain without the baggage of the knowledge of graphics processing.