Introduction
The powerful combination of Artificial Intelligence (AI), Edge Computing, and the Internet of Things (IoT) is transforming industries. AI empowers machines to learn from data, Edge Computing processes data close to its source, and IoT connects everyday devices for smarter interactions. By tapping into AI on the edge, companies in the IoT space can significantly cut down ongoing costs tied to data transmission and processing, ultimately enhancing their product profitability.
Challenges for IoT Companies: Data Transmission and Processing Costs
A major challenge for IoT companies is handling the ongoing costs associated with data transmission and processing. Transmitting data via cellular, LoRa, or satellite networks can become very expensive with large, data-intensive deployments. In some cases, these costs might even surpass the original bill of materials (BOM) cost of the device several times over. By incorporating AI in the device itself or on edge devices, the size and frequency of data sent to the cloud can be dramatically reduced, resulting in cost savings.
Another significant expense in IoT deployments is ingesting and processing data in the cloud, especially when using Platform as a Service (PaaS) solutions like AWS or Azure IoT Core offerings. By using the hardware you’ve already invested in to preprocess data with AI models, you can cut down on the amount of cloud processing you need to pay for. As this hardware often remains idle for extended periods, using it in this way is an efficient use of resources and reduces waste.
AI on the Edge: Options
For microcontroller space, TensorFlow Lite is a popular choice that supports Arduino, Zephyr, Espressif, and other ARM-based platforms. TensorFlow Lite enables you to run machine learning models on microcontrollers with high efficiency and minimal resource consumption.
In the embedded Linux context, you can access many well-known machine learning options, including TensorFlow, PyTorch, and scikit-learn. New AutoML options, such as Ludwig and auto-sklearn, can also be utilized in this space. One of the best practices for working with these options is to run an embedded Docker deployment, which helps isolate processes and simplifies managing dependencies.
Conclusion
In a nutshell, integrating AI on the edge can significantly boost IoT product profitability by reducing data transmission and cloud processing costs, which often far exceed the original BOM cost of the device. By exploring available options for AI on the edge and choosing the best fit for your project requirements, you can optimize your IoT product performance and profitability.
At Spinnaker Design, we offer hardware development services tailored to the needs of companies in the IoT space. Our team of experts is here to help you harness the power of AI on the edge to enhance your IoT product performance and profitability. Feel free to reach out and schedule a consultation with us to discuss how we can bring your IoT vision to life while maximizing your return on investment.