How businesses can make their products smarter with AI at the edge
AI continues to spark debate and demonstrate remarkable value for businesses and consumers. As with many emerging technologies, the spotlight often falls on large-scale, infrastructure-heavy, and power-hungry applications. However, as the use of AI grows, there is a mounting pressure on the grid from large data centers, with intensive applications becoming much less sustainable and affordable.
As a result, there is a soaring demand for nimbler, product-centric AI tools. Edge AI is leading this new trend, by bringing data processing closer to (or embedded within) devices, on the tiny edge, meaning that basic inference tasks can be performed locally. By not sending raw data off to the cloud via data centers, we are seeing significant security improvements in industrial and consumer applications of AI, which also enhances the performance and efficiency of devices at a fraction of the cost compared to cloud.
But, as with any new opportunity, there are fresh challenges. Product developers must now consider how to build the right infrastructure and the required expertise to capitalize on the potential of edge.The importance of local inference
Taking a step back, we can see that AI largely encompasses two fields: machine learning, where systems learn from data, and neural network computation, a specific model designed to think like a human brain. These are supplementary ways to program machines, training them to do a task by feeding it with relevant data to ensure outputs are accurate and reliable. These workloads are typically carried out at a huge scale, with comprehensive data center installations to make them function.
For smaller industrial use-cases and consumer industrial applications – whether this is a smart toaster in your kitchen or an autonomous robot on a factory floor – it is not economically (or environmentally) feasible to push the required data and analysis for AI inference to the cloud.
Instead, with edge AI presenting the opportunity of local inference, ultra-low latency, and smaller transmission loads, we can realize massive improvements to cost and power efficiency, while building new AI applications. We are already seeing edge AI contribute towards significant productivity improvements for smart buildings, asset tracking, and industrial applications. For example, industrial sensors can be accelerated with edge AI hardware for quicker fault detection, as well as predictive maintenance capabilities, to know when a device’s condition will change before a fault occurs.
Taking this further, the next generation of hardware products designed for edge AI will introduce specific adaptations for AI sub-systems to be part of the security architecture from the start.
