tinyML Summit 2023: A perspective on the trajectory from custom intelligent sensors to broad...
A perspective on the trajectory from custom intelligent sensors to broad market adoption of smart platforms
Al HESHMATI, VP of Systems and Software, TDK USA
While not always visible or appreciated by the end consumer users, there are already some good examples of intelligent use of sensors enabled by edge AI, utilizing machine learning techniques. We also see some examples of ML solutions deployed in industrial applications, including Condition Based Monitoring, quality control automation, and factory digitization. These solutions tend to be developed by teams including domain experts, sensor experts, data scientists and Machine Learning engineers. Depending on the application, it may also require close collaboration with HW/Silicon designers and platform SW developers. While this is reasonable for well-defined applications and by large companies with significant resources who can assemble such multi-disciplinary teams, it’s not practical for wide range of emerging applications. In this session, TDK, an industry-leading sensor provider to the IoT/tinyML community, shares its perspective on the trajectory for widespread adoption.
To enable broad market adoption and scaling, requires supporting the ecosystem with the level of integration at good cost/size/power that can be used to quickly enable new applications. The smart platforms including sensors, connectivity, processing, and power are common requirements for many IOT applications. Many sensor vendors, including TDK, are deploying eval kits intended to seed the developer community activities. There is an opportunity to deliver the product commercial HW platform that can more easily be utilized towards different end-applications. Over time, these designs can lead to more integrated solution where level of integration will be the balance between flexibility and cost/power/size optimization.
Broad enabling of Always-On, interactive apps and services will drive intelligence to the Edge, but many verticals with differing AI requirements exist. We can make an IOT device spanning multiple applications. However, the diversity of applications and their requirements means there is no “single AI solution” covering all applications. There is exciting SW work being done in the industry to enable tinyML applications more broadly. We already see several ML automation toolchains in the market focused on tinyML. Widespread adoption of smart sensor platforms requires seamless availability and optimized interworking of end-to-end elements from tool-chains to readily available ML-enabled HW platforms.
