Acoustic analog computing system based on labyrinthine metasurfaces

Abstract There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memory simultaneously.

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Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

Nature Communications ◽

10.1038/s41467-020-20257-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Shuiyuan Wang ◽

Lan Liu ◽

Lurong Gan ◽

Huawei Chen ◽

Xiang Hou ◽

...

Keyword(s):

Energy Efficiency ◽

Neural Plasticity ◽

Iris Recognition ◽

Computing System ◽

Internal Electric Field ◽

Fast Switching ◽

Analog Computing ◽

Non Volatile Memory ◽

Non Destructive ◽

With Memory

AbstractWith the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In2Se3 ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency.

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Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

10.21203/rs.3.rs-57108/v1 ◽

2020 ◽

Author(s):

Shuiyuan Wang ◽

Lan Liu ◽

Lurong Gan ◽

Huawei chen ◽

Xiang Hou ◽

...

Keyword(s):

Energy Efficiency ◽

Neural Plasticity ◽

Iris Recognition ◽

Computing System ◽

Internal Electric Field ◽

Fast Switching ◽

Analog Computing ◽

Non Volatile Memory ◽

Non Destructive ◽

With Memory

Abstract With the advent of big data era, applications are more data-centric, and energy efficiency issues caused by frequent data interactions due to the physical separation of memory and computing will become more severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In2Se3 ferroelectric semiconductor channel device that integrates non-volatile memory (NVM) and neural computation functions. Remarkable performance includes NVM ultra-fast write speed of 40ns, improved endurance through internal electric field, flexible adjustment of neural plasticity, energy consumption as low as 40fJ per event, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration laid the foundation for an integrated memory computing system with high density and energy efficiency.

Download Full-text