Long Live the “GPU”
This post is an articulation of an observation that a lot of the boom we are currently seeing around artificial intelligence/machine learning/deep learning will have the ultimate result that low power “GPUs” (Graphical Processing Units) will be everywhere. See https://www.gartner.com/smarterwithgartner/gartner-predicts-the-future-of-ai-technologies/.
Part of the logic behind the statement above relies on the realization that it is not always practical (and it is sometimes impossible) to send all the data required for Artificial Intelligence (AI) up to the cloud, where computing resources are more abundant. The sea of data just does not fit through the bandwidth straw up to and down from the cloud. See https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/. Also, reliable global communications are not here yet. Google reported in 2018 that 60% of mobile communications worldwide are over 2G. See https://www.youtube.com/watch?v=Hq026dfyIqU&t=85.
The logic behind the “GPU” statement above also derives from the fact that current machine learning/deep learning relies heavily on a lot matrix multiplications being performed really fast. But, the General Purpose Processors that are found on most electronic devices are not particularly good at matrix multiplications. On the other hand, Graphic Processing Units (GPUs), like those made by nVidia excel at parallel matrix multiplications.
While the GPU – or whatever marketing decides to call these fast matrix multiplication devices in the future – is a necessary component of modern day machine learning, a modern day GPU would not be realistic in everyday situations. For the AI powered by machine learning to be realistic with “just” our phones or at the “edge,” you also need the calculations made by these “GPUs” to consume low power. An nVidia GPU can consume 250 Watts - which is way too much for a low cost device running off a battery. For reference, the USB adapter for charging the iPhone is 5 Watts. See https://www.apple.com/shop/product/MD810LL/A/apple-5w-usb-power-adapter. Something like the Intel Neural Compute USB Stick is closer to what is needed. The Intel USB stick can produce results while using just 1 Watt.
To recap, I believe we will soon see a lot of low cost, low power, massively parallel computing devices capable of extremely fast matrix multiplications for the purpose of enabling Artificial Intelligence at the edge. I called the massively parallel computing devices “GPUs” in this post for lack of a better word.
Edit on February 14, 2019:
The article at https://www.technologyreview.com/s/612722/cheaper-ai-for-everyone-is-the-promise-with-intel-and-facebooks-new-chip/, which I just learned about, validates my post. It was published on January 7, 2019.
Edit on February 18, 2019:
The article Facebook joins Amazon and Google in AI chip race published by the Financial Times on February 19, 2019 in the UK (hence the time travel) is also related.