The other feature of endpoint AI that’s energizing developers is ease of design that’s leading to faster
proof-of-concept (PoC).

A majority of the respondents said they are developing their first PoCs within four weeks. The popular choices of hardware include development boards from silicon vendors, Raspberry Pi, existing custom hardware, and Arduino. Many developers chose to develop their first PoCs just on their development machine/PC because they don't want to be limited by the hardware constraints of the development boards in the early stage of development.

Some respondents agreed that development approaches, such as leveraging Raspberry Pi, were excellent for developing ideas quickly.

And often the development is model-first, in which ML engineers focus on the accuracy of the algorithm, experimenting with different ones to find the one with the best accuracy. This is not developed with particular hardware in mind. But once a team has settled on an algorithm, then hardware consideration begins.

The benefit of that dynamic is that production timelines are quickly shrinking. Nearly three-quarters of the respondents said they moved from concept to production in no more than a year. Additionally, there’s not much difference when it comes to industry segment how long it takes to move into production, with the notable exception of industrial (76 percent of industrial networking projects can be finished in six months, followed by manufacturing at roughly 60 percent).

41% of professional developers have tinyML projects in production

Real-world innovations

This acceleration of innovation is yielding results from tinyML in the real world. Consider Edge Impulse’s grid-monitoring system.

The RAM-1 – an intelligent grid sensor developed in collaboration between Edge Impulse, research institute IRNAS, and equipment manufacturer Izoelektro – seeks to help grids prevent fires from electrical faults before they happen. Specifically, the RAM-1 device analyzes electrical waveforms on an ongoing basis. If the AI algorithms on the device detect an anomaly, data from the select event is sent to the cloud for further classification. If a problem is indicated, warnings are relayed to control rooms and field crews. Integrating this functionality on an MCU means the product can operate for years on a single battery charge, which is an important feature on grids that are often miles from civilization.

Or consider Rainforest Connection’s bio-acoustic monitoring technology systems that seek to prevent illegal deforestation and halt animal poaching.

Rainforest Connection CEO Topher White with one of his devices.

In addition to those benefits, there’s another important one according to respondents: leaner teams. While teams of 11 or more tend to deliver production systems in six months or under, most teams numbering fewer than 10 developers are easily hitting year-and-under production schedules.

Vast range of use cases being explored professionally and for hobbyist projects