To Understand the Benefits of Edge AI, Just BLERP
BLERP?. It consist of five words:
Let's go through it, word by word.
Bandwidth
In many cases, though, there isn't enough bandwidth or energy budget available to send a constant stream of data to the cloud. That means that we will be forced to discard most of our sensor data, even though it contains useful signals.
Bandwidth limitations are very common. It's not just about available connectivity, it's also about power.
So, What if we could run the data analysis on the IoT device itself, without having to upload the data?
It is also quite common for devices to have no network connection at all.
Latency
Transmitting data takes time. Even if you have a lot of available bandwidth it can take tens or hundreds of miliseconds for a round-trip from a device to an internet server.
Economics
Connectivity costs a lot of money. Connected products are more expensive to use.
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By processing data on device, edge AI systems reduce or avoid the costs of transmitting data over a network and processing it in the cloud.
However, for products that depend on server-side AI, the cost of maintaining server-side infrastructure can complicate your business model.
Reliability
Many pieces of this puzzle are outside of your control, so even if you make all the right decision you will still be exposed to the reliability risk associated with technologies that make up your distributed computing stack.
It would be much safer if the AI is based entirely on-device, so it still operates in the event of a connectivity problem.
Privacy
When data is processed on an embedded system and is never transmitted to the cloud, user privacy is protected and there is less chance of abuse.
It's an especially important factor for applications in security, industry, childcare, education and healthcare. In fact, since some of these fields involve tight regulations (or customer expectations) around data security, the product with the best privacy is one that avoids collecting data altogether.
More?
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