An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs

Arnab Pal, Zichun Chai, Junkai Jiang, Wei Cao, Mike Davies, Vivek De and Kaustav Banerjee ()
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Arnab Pal: University of California
Zichun Chai: University of California
Junkai Jiang: University of California
Wei Cao: University of California
Mike Davies: Intel Labs
Vivek De: Intel Labs
Kaustav Banerjee: University of California

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Brain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages, frequencies, and activity factors, enabling two orders of magnitude higher energy-efficient computing that is difficult to achieve with conventional material and/or device platforms, specifically the silicon-based 7 nm low-standby-power FinFET technology. Our innovative 2D-TFET based NM circuit paves the way toward brain-like energy-efficient computing that can unleash major transformations in future AI and data analytics platforms.

Date: 2024
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DOI: 10.1038/s41467-024-46397-3

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