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Author: Tronserve admin

Monday 26th July 2021 11:48 PM

Giving IoT a Much Smarter Edge


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The internet of things (IoT) has for several years been touted as the answer to many challenges. Connected devices can improve efficiencies and productivity in industrial systems, provide valuable feedback mechanisms for connected healthcare systems and wearables, and provide a vast array of capabilities to improve driver assistance and enable a path towards autonomous driving in vehicles.

The promise has relied to a large extent on sensors in a network collecting data, transmitting it via a gateway to the cloud, processing that data, analyzing it, and then providing a control or feedback back to the local system or sensor.

However, over the last couple of years, developers and systems integrators have come to realize there are issues around latency, data security, and bandwidth cost in doing things this way. The way to mitigate against these challenges is to add more intelligence at the edge, so that response times are faster, data can be kept secure and private, and data communication costs are minimized. So putting intelligence at the edge is a no-brainer, right? Well it is, but there are of course practical limitations.

As edge intelligence has become the buzzword (or phrase) of the embedded systems industry, its definition has got rather fuzzy and it spans a wide part of the network. Some define the edge as anything not in the cloud; but even within edge, are you at the endpoint or the edge of the gateway? These are some of the questions where we try and understand if there is any consensus on the part of the vendors supplying the chips and systems.

In this special project, we also explore how much intelligence should be added to the edge, and what are the practicalities and limitations for doing so. Also, who are the top edge AI chip startups? We look at some of those, as well as predictions on the key trends around AI inferencing.


EETIMES



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Posted on : Monday 26th July 2021 11:48 PM

Giving IoT a Much Smarter Edge


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Posted by  Tronserve admin
image cap

The internet of things (IoT) has for several years been touted as the answer to many challenges. Connected devices can improve efficiencies and productivity in industrial systems, provide valuable feedback mechanisms for connected healthcare systems and wearables, and provide a vast array of capabilities to improve driver assistance and enable a path towards autonomous driving in vehicles.

The promise has relied to a large extent on sensors in a network collecting data, transmitting it via a gateway to the cloud, processing that data, analyzing it, and then providing a control or feedback back to the local system or sensor.

However, over the last couple of years, developers and systems integrators have come to realize there are issues around latency, data security, and bandwidth cost in doing things this way. The way to mitigate against these challenges is to add more intelligence at the edge, so that response times are faster, data can be kept secure and private, and data communication costs are minimized. So putting intelligence at the edge is a no-brainer, right? Well it is, but there are of course practical limitations.

As edge intelligence has become the buzzword (or phrase) of the embedded systems industry, its definition has got rather fuzzy and it spans a wide part of the network. Some define the edge as anything not in the cloud; but even within edge, are you at the endpoint or the edge of the gateway? These are some of the questions where we try and understand if there is any consensus on the part of the vendors supplying the chips and systems.

In this special project, we also explore how much intelligence should be added to the edge, and what are the practicalities and limitations for doing so. Also, who are the top edge AI chip startups? We look at some of those, as well as predictions on the key trends around AI inferencing.


EETIMES


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edge intelligence