Jongboom is an embedded engineer and machine learning advocate, always looking for ways to gather more intelligence from the real world.

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity.

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity. TinyML has made great progress in image classification, where the machine learning model must only predict the presence of a certain type of object in an image.

Zach Shelby has spent most of the last decade and a half on the front line of the Internet of Things (IoT). His company Sensinode, which was acquired by Arm in 2013, provided enterprise wireless sensor networks to system integrators and product providers. Shelby did lots of interesting work on embedded systems, and incorporating standards such as Bluetooth Low Energy (BLE). But he wanted to go a step further. The company, with Shelby as co-founder and CEO – Jan Jongboom, a colleague at Arm, as co-founder and CTO – is looking to enable developers to create next-gen applications with embedded machine learning (ML).

Markets are subject to fads and the embedded-control sector is far from immune to them. In the 1990s, fuzzy logic seemed to be the way forward and microcontroller (MCU) vendors scrambled to put support into their offerings only to see it flame out. Embedded machine learning (ML) is seeing a far bigger feeding frenzy as established MCU players and AI-acceleration start-ups try to demonstrate their commitment to the idea, which mostly goes under the banner of TinyML. Daniel Situnayake, founding TinyML engineer at software-tools company Edge Impulse and co-author of a renowned book on the technology, says the situation today is very different to that of the 1990s. "The exciting thing about embedded ML is that machine learning and deep learning are not new, unproven technologies - they've in fact been deployed successfully on server-class computers for a relatively long time, and are at the heart of a ton of successful products. Embedded ML is about applying a proven set of technologies to a new context that will enable many new applications that were not previously possible."