Tiny Machine Learning: The Next AI Revolution

Over the past decade, we have witnessed the size of machine learning algorithms grow exponentially due to improvements in processor speeds and the advent of big data. Initially, models were small enough to run on local machines using one or more cores within the central processing unit (CPU). Shortly after, computation using graphics processing units (GPUs) became necessary to handle larger datasets and became more readily available due to introduction of cloud-based services such as SaaS platforms (e.g., Google Colaboratory) and IaaS (e.g., Amazon EC2 Instances). At this time, algorithms could still be run on single machines. More recently, we have seen the development of specialized application-specific integrated circuits (ASICs) tensor processing units (TPUs) which can pack the power of 8 GPUs.