New technique enables on-device training using less than a quarter of a megabyte of memory
Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on “edge devices” that work independently from central computing resources.
Field programmable gate arrays (FPGAs) and microcontroller units (MCUs) are two types of commonly compared integrated circuits (ICs) that are typically used in embedded systems and digital design. Both FPGAs and microcontrollers can be thought of as “small computers” that can be integrated into devices and larger systems. As processors,…
A new technique enables on-device training of machine-learning models on edge devices like microcontrollers, which have very limited memory. This could allow edge devices to continually learn from new data, eliminating data privacy issues, while enabling user customization.
Neuromorphic devices, which are designed to emulate aspects of biological neural networks, are promising candidates for low-power, intelligent sensing technologies, including wearable applications.