ASICs, or Application Specific Integrated Circuits, are the next level of chip design – it’s a processor designed specifically for one type of task. The chip is architected to be very good at executing a specific function or type of function.
An awesome and relevant example is cryptocurrency mining, which people seem to attack with endless creativity. If you need an introduction to cryptocurrency, head over to Coindesk’s Blockchain 101 section. But for our purposes, all you need to know is this: mining these currencies on your computer involves brute force guessing of a specific number. It’s a pretty simple function, but the faster you do it the higher chance you have at winning; and there’s no variation. You just keep guessing until you get it right.
When you know there’s only going to be one specific computational requirement, you can design a chip around that specific task with its own unique architecture. For Bitcoin mining, one of these rigs is designed by a company called Bitmain. If you try to run other programs on it, it just won’t work.
Google has also gotten into the ASIC game, but with a focus on Machine Learning. It’s called a TPU (Tensor Processing Unit), and it’s a Google-designed and manufactured chip specifically made for Machine Learning with Tensorflow, Google’s open source deep learning framework. Google claims that these TPUs can be up to 15x to 30x faster than the best CPUs and GPUs for programming Neural Nets, but there has been some dispute as to how accurate that figure really is. A 3rd party benchmark was recently released and found that TPUs can be significantly more efficient than comparable GPUs.
As Machine Learning becomes more and more integrated into all the applications we use on a daily basis, expect more research to be done on how to create chips tailored for these tasks.
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