A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning.
The edge-specialized TPU is an ASIC chip, a breed of chip architecture that’s increasingly popular for specific use cases like mining for cryptocurrency (such as larger companies like Bitmain). The chips excel at doing specific things really well, and it’s opened up an opportunity to tap various niches, such as mining cryptocurrency, with specific chips that are optimized for those calculations. These kinds of edge-focused chips tend to do a lot of low-precision calculations very fast, making the whole process of juggling runs between memory and the actual core significantly less complicated and consuming less power as a result.
The hardware is designed for enterprise applications, like automating quality control checks in a factory
The Edge TPU is designed to do what’s known as “inference.” This is the part of machine learning where an algorithm actually carries out the task it was trained to do; like, for example, recognizing an object in a picture. Google’s server-based TPUs are optimized for the training part of this process, while these new Edge TPUs will do the inference.
These new chips are destined to be used in enterprise jobs, not your next smartphone. That means tasks like automating quality control checks in factories. Doing this sort of job on-device has a number of advantages over using hardware that has to sent data over the internet for analysis. On-device machine learning is generally more secure; experiences less downtime; and delivers faster results. That’s the sales pitch anyway.
Google isn’t the only company designing chips for this sort of on-device AI task though. ARM, Qualcomm, Mediatek and others all make their own AI accelerators, while GPUs made by Nvidia famously dominate the market for training algorithms.
However, what Google has that its rivals don’t is control of the whole AI stack. A customer can store their data on Google’s Cloud; train their algorithms using TPUs; and then carry out on-device inference using the new Edge TPUs. And, more than likely, they’ll be creating their machine learning software using TensorFlow — a coding framework created and operated by Google.
For more info about this you can go through here https://blog.google/products/google-cloud/bringing-intelligence-to-the-edge-with-cloud-iot/
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