"This means the market will soon start to see multiple AI chipsets in a single device at sensor and device level," says Su.
More importantly, says the firm, it is not just hardware development that accelerates the democratization of TinyML. Open-source software development from Google through TensorFlow Lite for microcontroller and proprietary solutions from the likes of SensiML offer developer-friendly software tools and libraries, allowing more AI developers to create AI models that can support very edge applications.
TinyML chipset manufacturers, says the firm, must focus on developing their AI developer ecosystem or be part of existing ecosystems, embrace open source, and focus on articulating their unique selling points and target markets to end users. Without these conditions, chipset suppliers will struggle to generate scale for their products in what is expected to be a very competitive market.
"At the moment most of these solutions are still in the early stages of commercial deployment in smart cities and smart manufacturing, mainly used for asset tracking and anomaly sensing, and yet to achieve large-scale adoption," says Su. "While able to offer better processing capabilities, sensors with TinyML are often much more expensive. End users will also need to design and introduce a new set of procedures and protocols to leverage the information and insights derived from these sensors."
For more, see " Very Edge AI Chipsets for TinyML Applications ."