Engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet.
Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence tasks in real time without beaming data to the cloud for analysis. With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics for real-time data processing and near-instant diagnostics.
A nanotechnology expert, Hersam is Walter P. Murphy Professor of Materials Science and Engineering at Northwestern's McCormick School of Engineering. He also is chair of the Department of Materials Science and Engineering, director of the Materials Research Science and Engineering Center and member of the International Institute of Nanotechnology.
"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."
"Every time data are passed around, it increases the likelihood of the data being stolen," Hersam said."If personal health data is 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."
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