K
Kathleen Martin
Guest
Artificial intelligence (AI) at the edge of the network is a cornerstone that will influence the future direction of the technology industry. If AI is an engine of change, then semiconductors are the oil driving the new age that is being defined by machine learning (ML), neural networks, 5G connectivity and the advent of blockchain, digital twins and the metaverse.
Despite recent disruptions to the chip industry due to supply chain and more recently, macroeconomic factors, the confluence of AI – and the Internet of Things (IoT) known as AIoT– is poised to shift the world from cloud-centric intelligence to a more distributed intelligence architecture.
It is expected that a staggering 73.1 zettabytes of data is expected to be generated by IoT devices, in 2025 according to IDC Research. As a result, endpoint data will increase at a CAGR of 85% from 2017 to 2025, driving intelligence from the cloud to the endpoint to run AI/ML workloads within tiny machines (TinyML). Some of the applications that are seeing the most disruption include the development of “voice as a user interface” to improve human-to-machine communication, as well as environmental sensing and predictive analytics and maintenance. Major growth segments include wearables, smart homes, smart cities and intelligent industrial automation.
What are the benefits of embedding intelligence at the endpoint? Many industrial IoT applications operate within environments constrained by memory capacity, limited computing and battery power and sub-optimal connectivity. Moreover, these applications often require real-time responses that may be mission and system critical. Expecting such devices and applications to operate in a cloud-centric intelligence architecture just does not work.
This is where the power of embedding intelligence at the endpoint is evolving from standard industrial IoT implementations to what we are calling AIoT for industrial applications.
Transforming data at the source of collection minimizes latency and enables optimized processing for time-critical applications. Because data is not processed and transported over the network, the security concerns related to transfer and flow of data, are greatly minimized. Another advantage is that data handling, can be linked with root-of-trust at the endpoint, making the implementation impervious to attacks. Since data processing is handled at or very near to the source, we can fully leverage data gravity and reduce the power consumption associated with turning on radios or moving data through the network.
Our commitment to our customers is to lead the industry in endpoint compute technology with the broadest range of MCUs and MPUs. Already this has enabled designers to leverage our rich ecosystem of IoT and AI/ML building blocks by tapping into a technology ecosystem that features more than 300 building blocks of commercial grade software provided by Renesas’s trusted partners.
Continue reading: https://www.renesas.com/us/en/blogs/executive-blog-ai-iot-edge-disrupting-industrial-market
Despite recent disruptions to the chip industry due to supply chain and more recently, macroeconomic factors, the confluence of AI – and the Internet of Things (IoT) known as AIoT– is poised to shift the world from cloud-centric intelligence to a more distributed intelligence architecture.
It is expected that a staggering 73.1 zettabytes of data is expected to be generated by IoT devices, in 2025 according to IDC Research. As a result, endpoint data will increase at a CAGR of 85% from 2017 to 2025, driving intelligence from the cloud to the endpoint to run AI/ML workloads within tiny machines (TinyML). Some of the applications that are seeing the most disruption include the development of “voice as a user interface” to improve human-to-machine communication, as well as environmental sensing and predictive analytics and maintenance. Major growth segments include wearables, smart homes, smart cities and intelligent industrial automation.
What are the benefits of embedding intelligence at the endpoint? Many industrial IoT applications operate within environments constrained by memory capacity, limited computing and battery power and sub-optimal connectivity. Moreover, these applications often require real-time responses that may be mission and system critical. Expecting such devices and applications to operate in a cloud-centric intelligence architecture just does not work.
This is where the power of embedding intelligence at the endpoint is evolving from standard industrial IoT implementations to what we are calling AIoT for industrial applications.
Transforming data at the source of collection minimizes latency and enables optimized processing for time-critical applications. Because data is not processed and transported over the network, the security concerns related to transfer and flow of data, are greatly minimized. Another advantage is that data handling, can be linked with root-of-trust at the endpoint, making the implementation impervious to attacks. Since data processing is handled at or very near to the source, we can fully leverage data gravity and reduce the power consumption associated with turning on radios or moving data through the network.
Our commitment to our customers is to lead the industry in endpoint compute technology with the broadest range of MCUs and MPUs. Already this has enabled designers to leverage our rich ecosystem of IoT and AI/ML building blocks by tapping into a technology ecosystem that features more than 300 building blocks of commercial grade software provided by Renesas’s trusted partners.
Continue reading: https://www.renesas.com/us/en/blogs/executive-blog-ai-iot-edge-disrupting-industrial-market
