What is embedded machine learning?

Data transmission and computational power. That’s AI in a nutshell. It’s computationally expensive to implement and gets in the way of computers doing other work. Now a new generation of AI threatens to remove these roadblocks and usher in a better AI future. It’s called embedded machine learning.

What is embedded ML?

Embedded machine learning moves AI computation from a remote computer to the device. That could be IoT-enabled components or machines, for instance. To date, it hasn’t been practically possible to get enough computational power into IoT devices. That could be about to change as more powerful dedicated IoT AI chips come to market.

What’s the benefit of embedded machine learning?

Embedded ML could allow more widespread use of IoT, with efficiency improvements that allow for the equivalent of 25 per cent faster execution times. It would improve visibility on a machine by machine basis and harvest data that is currently excluded owing to cost or bandwidth constraints.

Embedded machine learning will free up budgets and bandwidth

Too much data is a bad thing when you can’t collect it all. Current AI models get by on what data they are allowed to collect, as chosen by humans driven by cost-saving briefs or limited allocations of bandwidth. Without those constraints, embedded machine learning is set to accelerate as reasons to limit data transfer and computational power fall.

Embedded machine learning everywhere

Embedded AI chips could find their way into everything – vehicles, plant, data centres, arms – and spark a wave of automatic data collection under their own computational steam.