Enabling Smarter IoT Devices With Endpoint AI
Thanks to the Internet of Things (IoT), there are more connected devices than ever around us. Wearable fitness trackers, smart home appliances, and industrial control equipment are some common examples of connected devices making a sizable impact in our lives.
What used to be simple, self-contained machines are turning into intelligent devices that can talk with other devices and act in real-time. Known as endpoint AI, the convergence of on-device intelligence with artificial intelligence (AI) and machine learning (ML) capabilities, has the potential to unlock new use cases and applications.
In this article, we will breakdown endpoints, why they need to be smart, and the benefits of endpoint AI for your organization.
The term “endpoint” describes a remote computing device at the end of a communication network. Endpoints are made up of devices such as desktops, laptops, smartphones, tablets, servers, workstations, and the Internet-of-things (IoT) devices.
Endpoints that are continually plugged into an AC outlet can perform many types of applications and functions, as they are not limited by the amount of power they can use. In contrast, endpoint devices deployed out in the field are designed to perform very specific and limited functions.
To handle various applications, IoT endpoints require a microcontroller-based processing device that can be programmed to execute a desired computational functionality, such as temperature or moisture sensing. Because these endpoint devices are battery-powered, the microcontroller unit responsible for processing various applications must run on ultra-low power.
IoT endpoint devices are useful for monitoring services, processes, or machines, by regularly collecting data and sending it to other parts of the IoT network. From our home and healthcare, to smart cities, industrial, and retail spaces, IoT is enriching and transforming our daily lives.
The Need for Endpoint Intelligence
New IoT applications in various industries are generating tons of data, and to extract actionable value from it, we can no longer rely on sending all the data back to cloud servers. As the number of IoT devices increase, so does the amount of data needing to be transmitted. Unfortunately, sending massive amounts of data to the cloud is unsustainable.
There are some significant costs that come up when transferring data from endpoints to the cloud, including data transmission energy, longer latency, bandwidth, and server capacity which are all factors that can wipe out the value of any use case. IoT applications rely heavily on data analytics and real-time decision making at the lowest latency possible.
Some endpoints are deployed in remote locations and may only have limited or periodic connectivity. Because of this, the right processing capabilities must be made available in the right place. In other words, intelligence must be available across the network all the way to the endpoint at the source of the data. By increasing the on-device compute capabilities, we can better unlock real-time data analytics in IoT endpoints.
The Benefits of Endpoint AI
IoT endpoint devices are generating massive amounts of sensor data and real-time information. Without an endpoint AI to process this data, much of it would be discarded because it costs too much in terms of energy and bandwidth to transmit it.
Through edge computing, endpoint AI allows your business analytics to be performed on devices at the edge of the network, where the data is collected from IoT devices like sensors and on-machine applications.
The ability to perform advanced localized processing closer to where data is collected results in faster and more accurate responses, which allows you to maximize any data insights. Other benefits include an improved performance across the overall system, reduced power budget, and reduced reliance on cloud processing.
How Ambiq is Helping
Ambiq’s ultra-low-power wireless MCUs and SoCs are accelerating edge inference in devices limited by size and power. Our products enable IoT companies to deliver solutions with a much longer battery life and more complex, faster, and advanced ML algorithms right at the endpoint.
Built on our patented Subthreshold Power Optimized Technology (SPOT) platform, Ambiq’s products reduce the total system power consumption on the order of nanoamps for all battery-powered endpoint devices. Simply put, our solutions can enable intelligence everywhere.