TinyML enables the performance of data analytics on low-powered hardware with low processing power and small memory size, aided by software designed for small-sized inference workloads
Between 2021 and 2026, the number of IoT connections will nearly triple to 23.6 billion. Each new connection, as per ABI Research, represents an opportunity to leverage Artificial Intelligence (AI) and Machine Learning (ML), and TinyML technology will be pivotal in seizing that opportunity.
“The proliferation and democratization of AI has fueled the growth of Internet of Things (IoT) analytics. Data collected from IoT devices are used to train Machine Learning (ML) models, generating valuable new insights into the IoT overall. These applications require powerful and expensive solutions that rely on complex chipsets,” explains Lian Jye Su, AI & ML Principal Analyst at ABI Research.
Su added, “At the same time, edge AI chipsets have introduced AI to myriad end points, including mobile devices, automobiles, smart home speakers, and wireless cameras. However, these devices are often too underpowered to make use of all the data flowing across them and struggle to support high computing performance and high data throughput, causing latency issues, which is a death knell for AI.”
Cost and power efficient
The TinyML market, as per the report, will grow from 15.2 million shipments in 2020 to 2.5 billion in 2030. TinyML chipsets aim to solve the issues of both cost and power efficiency. TinyML enables the performance of data analytics on low-powered hardware with low processing power and small memory size, aided by software designed for small-sized inference workloads. It has the potential to revolutionize the future of the IoT. The proliferation of TinyML will lead to the expansion of edge AI beyond traditional key markets.
“By bringing AI analytics beyond machine vision, more end users can benefit from smart connected sensors and IoT devices based on soundwaves, temperature, pressure, vibration, and other data sources,” Su says.
This growth offers tangible and important benefits such as data privacy, high interconnectivity and interaction of various components, high-energy efficiency, small chipset footprint, functional safety, and security concerns, and overcoming network bandwidth challenges.
“Given that very edge AI exists in a slightly more specialized environment as compared to the general edge AI, end users must prepare their equipment, connectivity infrastructure, and internal expertise to capture the real benefits of very edge AI. Knowing the user demand and requirements for TinyML, TinyML AI chipset vendors need to differentiate through key use cases and focus on software and services,” Su concludes.
