TinyML to proliferate in 2022

December 21, 2021//By Rich Pell
TinyML to proliferate in 2022
A new whitepaper from market research firm ABI Research identifies artificial intelligence/machine learning (AI/ML) market trends that it says will deliver in 2022 - and some that won't.

The whitepaper identifies 35 trends that will shape the technology market and 35 others that, although attracting huge amounts of speculation and commentary, are less likely to move the needle over the next twelve months, says the firm.

"The fallout from COVID-19 prevention measures, the process of transitioning from pandemic to endemic disease, and global political tensions weigh heavily on the coming year's fortunes," says says Stuart Carlaw, Chief Research Officer at ABI Research. "This whitepaper is a tool for our readers to help shape their understanding of the key critical trends that look set to materialize in 2022 as the world begins to emerge from the shadow of COVID-19. It also highlights those much-vaunted trends that are less likely to have meaningful impact in 2022."

In the paper, the firm's analysts forecast that TinyML, which is already showing massive potential, will be on the path to becoming the largest segment of the edge Machine Learning (ML) market by shipment volume. Total shipments of 1.2 billion devices with TinyML chipsets are forecast in 2022.

This means more devices will be shipped with TinyML chipsets, as compared to those with edge ML chipsets, says the firm. In addition, the proliferation of ultra-low-power ML applications means more brownfield devices will also be equipped with ML models for on-device anomaly detection, condition monitoring, and predictive maintenance.

说,2022年不找什么公司,是一个single regulation to govern AI. While more and more countries are preparing their regulations to govern the design, development, and deployment of AI, ABI Research analysts believe that no nation will rely on a single regulation to govern AI.

Instead, countries are expected to develop guidelines, standards, and regulations to oversee various aspects of AI, including data collection, storage, model transparency, future update, and legal responsibilities. A good example will be the EU, which relies on General Data Protection Regulation (GDPR), ethics governance framework, risk-based


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