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embedded machine learning

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Designed to bring machine learning to the Earth's last great frontier, a prototype smart sensor platform users on-device inference to classify marine mammal calls — powering itself exclusively from the sounds it captures.

Ultra-efficient machine learning offers on-device inference powered solely by sound - under the sea

The CyberSpec framework is designed to detect anomalous behavior linked to cyber-attacks against crowd-sensing spectrum sensors, even when said sensors are running on lightweight resource-constrained hardware like a Raspberry Pi.

CyberSpec turns machine learning onto the problem of spectrum sensor attacks, without overloading the host

In this article, we take a look at two tinyML projects related to healthcare. The first project helps gather Mean Radiant Temperature data outdoors to protect people from extreme heat, and the other one is a solution for affordable, accurate, & rapid detection of pneumonia.

Applications of TinyML in healthcare for a safer future

In this article, we look at two tinyML projects for education. We show how Backyard Brains uses low-cost experiment kits to make neuroscience education more accessible. We also introduce our readers to a specialisation offered by Harvard & Google to help students learn tinyML like never before.

Applications of TinyML making specialised education more accessible

CFU Playground: Customize and co-optimize your ML processor to bring Tiny Machine Learning Acceleration on FPGA

Iterative Design Approach of ML Accelerators FPGA-based RISC-V Core

A holistic open-source framework to improve the performance of multiple modalities and outperform the existing single-modality algorithms.

Meta AI Introduces Data2vec Open-Source Framework for Self-Supervised Learning in NLP, Speech and Vision Processing

In this article, we take a look at two tinyML projects that have the potential to make contributions towards sustainable development goals. While the first project is about revolutionising precision farming, the second one aims to create a network of low-cost sensors for mapping carbon emissions.

TinyML unlocks new possibilities for sustainable development technologies

Edge Impulse recently announced official support for the Syntiant NDP101 processor, an always-on sensor and speech recognition processor, with ultra-low power consumption of less than 140 uW while recognizing words.

Build Speech, Audio and Sensor Machine Learning Applications with Syntiant's TinyML Platform and Edge Impulse

The article covers the basics of TinyML, a technology that improves the privacy, energy efficiency, affordability and reliability of devices utilising artificial intelligence. Be a part of TinyML for good, an online event and showcase of ideas that aims at exploring the topic in more detail.

TinyML for Good: where machine learning meets edge computing

A closer look at the recent market trends and audio technologies that enhance the functionalities of TWS devices.

TWS Evolution: What is next in True Wireless Audio?

Making the leap from “Smart” to “Useful”. Smart home devices will continue to increase their utility as the challenges of audio source classification are solved by audio edge processors specifically designed for advanced audio and machine learning applications.

Enhancing the utility of Audio AI in everyday devices using Audio Source Classification

#4 of our 'Voice of Innovation' fireside chat series, in which Syntiant CEO Kurt Busch talks with Andreas Urschitz Division President Power & Sensor Systems at Infineon.

Fireside Chat With Andreas Urschitz from Infineon


An overview of the architecture of IIoT and how it is developing to drive the scalability of Industry 4.0.

The engineer's guide to Industrial IoT and Industry 4.0

From Single Models to Full MLOps, Edge Impulse Is The New Instruction Engine For A Smarter Industrial Sector.

Embedded Machine Learning Is Giving Industrial Machines The Brains They Always Wanted.

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