Categories: AI/ML News

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) vs. microcontrollers: What’s the difference?

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,…

June 4, 2024

In "FAANG"

AI models can now continually learn from new data on intelligent edge devices like smartphones and sensors

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.

October 5, 2022

In "AI/ML News"

New chip design to provide greatest precision in memory to date

Everyone is talking about the newest AI and the power of neural networks, forgetting that software is limited by the hardware on which it runs. But it is hardware, says USC Professor of Electrical and Computer Engineering Joshua Yang, that has become "the bottleneck." Now, Yang's new research with collaborators…

March 30, 2023

In "AI/ML News"

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