Abstract

The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralised in industrialised nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML). We’re most familiar with consumer-facing applications such as Siri, Alexa, and Google Assistant, but the limited cost and small size of such devices allow them to be deployed in the field. For example, the technology has been used to detect mosquito wingbeats and so help prevent the spread of malaria. It’s also been part of the development of low-power animal collars to support conservation efforts.