Cartoon: Working at the edge
“Working at the edge” by Iantoons
This new cartoon illustrates how nature can perhaps be a guide on how to improve intelligence at the edge of computing networks.
The debate about where to place computing intelligence – either at the centre or the edge of a network – has gone on for over 50 years.
With the advances in machine learning, this debate has shifted to what level of localised control should be allowed by TinyML. TinyML is a type of machine learning that allows models to run on smaller, less powerful devices, which only take up a small footprint.
Example TinyML use cases are predictive maintenance, image traffic optimisation, noise monitoring, and other services that can run locally on distributed sensors and cameras without needing to take direction from a central database.
Consumer use cases today include keyword spotting in voice technology solutions. For example, when we call, “Hey Siri…”, a TinyML function with low battery/intelligence usage wakes up the central database.
However, some of the challenges of TinyML include the fragmented hardware environment and working out how to get different devices with TinyML to work together.
One area that might prove instructive to this issue is how ants organise – making localised decisions and using antenna touches to communicate with each other.
According to Professor Bernd Meyer from the University of Monash in Australia, ants “make quite complicated decisions without the help of a logistics expert”. He adds: “The way ants organise themselves can give us insights into things like better traffic flow management and factory floor optimisation.”
You can find more Iantoons cartoons here.
