Development is moving quickly from cloud to endpoints
The arc of AI adoption has been decades in the making, but in just the past few years the flood gates of innovation have burst open, first around AI development in the cloud and now out at the endpoints, where the real world meets the digital. This rapid adoption is a testament to the early successes of AI and ML but also of the robust, heterogeneous IP, tools offerings and developer ecosystem that are fast emerging.
This is clearly reflected in the survey results: Embedded developers are quickly embracing the power and potential of tinyML on endpoints as a new way to design more intelligent autonomous systems. AI developers with data expertise are driving adoption of tinyML as well. This transformation is yielding much faster prototypes and much shorter time-to-market for software development teams.
Strong correlation between data science, DSP skills and embedded skills for professional developers.
However, nearly half the respondents struggle with modeling optimization, and best practices are not yet cemented around tools and educational channels. In short, opportunity awaits.
Embedded developers and data scientists converge on the same point.
As endpoint design is fundamentally an embedded application, it’s perhaps no surprise that embedded designers and developers are embracing tinyML at the endpoint. Most of the respondents are well versed in Python, C++ and embedded software, and they have a strong correlation between data science expertise and DSP skills.
Experienced embedded designers are devouring information to learn more about data and the discipline of data science to expand their skills in this area.
Popular tinyML implementations include sensor fusion, object classification and anomaly detection.
The development of endpoint AI also doesn’t appear to have created siloed areas of expertise inside companies, according to the data. A significant number of developers are involved in both the data and modelling sides of the development equation. Nearly 60 percent are involved in the system architecture development, and a similar number are also involved in data pre-processing and feature extraction as well as training and ML model experimentation. And while that might suggest that that cohort has more expertise in data science, that’s the not the case: Those with strong embedded skills but weaker data expertise are just as likely to be involved in data processing and model training as their data-science expert counterparts.
Gauging the long-term success and popularity of any emerging technology can usually be judged by the breadth of early applications, and endpoint AI may be a technology with some of the broadest application that we’ve seen in decades.
Teams are implementing tinyML in many different use cases, including face, object, anomaly and gesture detection, biometric awareness, speech recognition object classification, real-time recognition and more. The most popular tinyML implementations include sensor fusion, object classification and anomaly detection.
Implementation is also spread broadly into vertical segments, although the most popular today are smart cities and homes, wearables and electronics, industrial automation and healthcare.
