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New Architectural Horizons for Nanoscale Neuromorphic Computing

Novel liquid film has the potential to function as optical memory, thereby allowing new architectural horizons for nanoscale neuromorphic computing.

New Architectural Horizons for Nanoscale Neuromorphic Computing.
Simulation result of light affecting liquid geometry, which in turn affects reflection and transmission properties of the optical mode, thus constituting a two-way light–liquid interaction mechanism. The degree of deformation serves as an optical memory allowing to store the power magnitude of the previous optical pulse and use fluid dynamics to affect the subsequent optical pulse at the same actuation region, thus constituting an architecture where memory is part of the computation process. Credit: Gao et al.

Sunlight sparkling on the water evokes the rich phenomena of liquid–light interaction, covering temporal and spatial scales. As the dynamics of liquids have mesmerized scientists for around 10 years, the increase in neuromorphic computing has triggered considerable measures to come up with new and unconventional computational schemes.

This relies on recurrent neural networks, crucial to assisting an extensive range of modern technological applications, like autonomous driving and pattern recognition.

Furthermore, as biological neurons also depend on a liquid surrounding, a convergence could be obtained by bringing nanoscale nonlinear fluid dynamics to neuromorphic computing.

At the University of California San Diego, scientists recently suggested a novel paradigm where liquids, which generally do not cooperate with the light on a micro- or nanoscale, aid considerable nonlinear response to optical fields.

As reported in the journal Advanced Photonics, the scientists anticipate a considerable light–liquid interaction effect via a suggested nanoscale gold patch functioning as an optical heater and producing thickness changes in a liquid film that covers the waveguide.

The liquid film essentially operates as an optical memory. The study illustrates its working: Light in the waveguide tends to impact the geometry of the liquid surface, while variations in the shape of the liquid surface impact the properties of the optical mode in the waveguide. This thus constitutes a mutual coupling between the liquid film and the optical mode.

Moreover, as changes are observed in the liquid geometry, the properties of the optical mode experience a nonlinear response; following the optical pulse stops, the magnitude of the liquid film’s deformation denotes the power of the earlier optical pulse.

Unlike conventional computational approaches, the nonlinear response and the memory dwell in the same spatial region, implying the realization of a small (over von-Neumann) architecture where memory and computational unit occupy the same space.

The scientists illustrate that the combination of nonlinearity and memory enables the feasibility of so-called “reservoir computing” capable of executing digital and analog tasks, like handwritten image recognition and nonlinear logic gates.

Also, their model exploits one more considerable liquid feature called nonlocality. This allows them to forecast computation improvement that is just not possible in solid state material platforms with restricted nonlocal spatial scales.

In spite of nonlocality, the model does not quite obtain the levels of modern solid-state optics-based reservoir computing systems. However, the work still presents a clear roadmap for future experimental works that target to verify the anticipated impacts and study intricate coupling mechanisms of several physical processes in a liquid environment for computation.

By making use of multiphysics simulations to examine the coupling between heat transport, fluid dynamics, light, and surface tension effects, the scientists anticipate a family of novel nonlocal and nonlinear optical effects. They take their work a step further by denoting how these could be utilized to identify adaptable and non-traditional computational platforms.

The researchers indicate enhancements to sophisticated liquid-assisted computation platforms by nearly five orders of magnitude in space and at a minimum of two orders of magnitude in speed, taking advantage of a mature silicon photonics platform.

Video Credit: SPIE.

Journal Reference:

Gao, C., et al. (2022) Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer. Advanced Photonics.


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