Engineers at Northwestern University in Illinois have developed a nanoelectronic device that can perform accurate machine-learning tasks in the most energy-efficient manner yet, making it suitable for health wearables.

Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.

With its tiny footprint, low power consumption and lack of lag time to receive analyses, the device is suitable for direct incorporation into wearable electronics such as smart watches and fitness trackers for real-time data processing and near-instant diagnostics.

To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy.

The research was published in the journal Nature Electronics (www.nature.com/articles/s41928-023-01042-7).

“Today, most sensors collect data and then send them to the cloud, where the analysis occurs on energy-hungry servers before the results are finally sent back to the user,” said Northwestern’s Mark Hersam, the study’s senior author. “This approach is incredibly expensive, consumes significant energy and adds a time delay. Our device is so energy efficient that it can be deployed directly in wearable electronics for real-time detection and data processing, enabling more rapid intervention for health emergencies.”

Hersam co-led the research with Han Wang, a professor at the University of Southern California, and Vinod Sangwan, a research assistant professor at Northwestern.

Before machine-learning tools can analyse new data, these tools must first accurately and reliably sort training data into various categories. For example, if a tool is sorting photos by colour, then it needs to recognise which photos are red, yellow or blue to classify them accurately. An easy chore for a human, but a complicated and energy-hungry job for a machine.

For current silicon-based technologies to categorise data from large sets such as ECGs, it takes more than 100 transistors, each requiring its own energy to run. But Northwestern’s nanoelectronic device can perform the same machine-learning classification with just two devices. By reducing the number of devices, the researchers reduced power consumption and developed a much smaller device that can be integrated into a standard wearable gadget.

The secret behind the novel device is its tunability, which arises from a mix of materials. While traditional technologies use silicon, the researchers constructed the miniaturised transistors from two-dimensional molybdenum disulphide and one-dimensional carbon nanotubes. So instead of needing many silicon transistors – one for each step of data processing – the reconfigurable transistors are dynamic enough to switch among various steps.

“The integration of two disparate materials into one device allows us to strongly modulate the current flow with applied voltages, enabling dynamic reconfigurability,” Hersam said. “Having a high degree of tunability in a single device allows us to perform sophisticated classification algorithms with a small footprint and low energy consumption.”

To test the device, the researchers looked to publicly available medical datasets. They first trained the device to interpret data from ECGs, a task that typically requires significant time from trained healthcare workers. Then, they asked the device to classify six types of heart beats: normal, atrial premature beat, premature ventricular contraction, paced beat, left bundle branch block beat and right bundle branch block beat.

The nanoelectronic device was able to identify accurately each arrhythmia type out of 10,000 ECG samples. By bypassing the need to send data to the cloud, the device not only saves critical time for a patient but also protects privacy.

“Every time data are passed around, it increases the likelihood of the data being stolen,” Hersam said. “If personal health data are processed locally – such as on your wrist in your watch – that presents a much lower security risk. In this manner, our device improves privacy and reduces the risk of a breach.”

Hersam imagines that, eventually, these nanoelectronic devices could be incorporated into everyday wearables, personalised to each user’s health profile for real-time applications. They would enable people to make the most of the data they already collect without sapping power.

“Artificial intelligence tools are consuming an increasing fraction of the power grid,” Hersam said. “It is an unsustainable path if we continue relying on conventional computer hardware.”

The study was supported by the US Department of Energy, National Science Foundation and Army Research Office.