As TinyML vendors continue to democratize Machine Learning (ML) at a rapid pace, global technology intelligence firm ABI Research forecasts that TinyML Software-as-a-Service (SaaS) revenue will exceed US$220 million in 2022 and become an important component from 2025 onward.
While total revenue will be dominated by chipset sales, as TinyML device shipments continue to grow, the TinyML SaaS and professional service market have the potential to become a billion-dollar market by 2030, ABI Research adds.
ABI Research is a global technology intelligence firm delivering actionable research and strategic guidance to technology leaders, innovators, and decision-makers worldwide. The above findings are from ABI Research's TinyML: A Market Update application analysis report, which is a part of the company's AI and ML research service.
The TinyML market has come a long way since ABI Research first analyzed this market back in 2020.
The TinyML Foundation, which gathers most of the prominent vendors in this space, has substantially expanded in recent years.
Similar expansion has been in the applications of TinyML; with forest fire detection, shape detection, and seizure detection among some of the most spectacular use cases.
Moreover, given how central environmental sensors are to TinyML, the possibilities are extensive.
Nonetheless, ambient sensing and audio processing remain the most common applications in TinyML, with sound architectures holding an almost 50% market share in 2022. Most of these applications employ either a microcontroller (MCU) or an Application-Specific Integrated Circuit (ASIC).
The personal and work devices sector will be the most significant increase in the near future.
"Any sensory data from an environment can probably have an ML model applied to that data. Some of the most common applications include word spotting, object recognition, object counting, and audio or voice detection," explains David Lobina, Artificial Intelligence and Machine Learning Research Analyst at ABI Research.
With the myriad possibilities, there are also potential pitfalls, but for which ABI Research believes there are well-identified solutions.
"The physical constraints on TinyML devices are genuine. These devices favour small and compact ML models, which call for innovation at the software solutions level for specific use cases. And software providers will be the most active in the TinyML market," says Lobina. Software providers in this space include Edge Impulse, SensiML, Neuton, Nota, and Deeplite.
In addition, considering the vast number of use cases, vendors must concentrate on those applications that TinyML has a clear value proposition worked out before production.
"The role of software is crucial, and vendors must develop software tools to automate TinyML itself. Finally, new technology will be required to bring about ever more sophisticated TinyML models. Neuromorphic computing and chips, along with the corresponding technique of Spiking Neural Networks, would bode well for the future," adds Lobina.
