Experimental Demonstration of In‐Memory Computing in a Ferrofluid System (Adv. Mater. 23/2023)

Crepaldi, Marco; Mohan, Charanraj; Garofalo, Erik; Adamatzky, Andrew; Szaciłowski, Konrad; Chiolerio, Alessandro

doi:10.1002/adma.202370165

Cited by 1 publication

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“…Reservoir computing employs a fixed-interconnection network as the reservoir and only requires training for the readout layer, resulting in low training cost. Moreover, due to their fixed nature, their physical implementation becomes very promising, which opens up the excellent possibility of using mechanical systems to conduct computing, [5,[22][23][24][25][26][27] ideal for our proposed MI. However, despite the great potential of the PRC framework, the efforts have mostly focused on computation only, and have not yet been utilized to construct MI to enable engineering-relevant functionalities.…”

Section: Introductionmentioning

confidence: 99%

Embodying Multifunctional Mechano‐Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing

Zhang,

Deshmukh,

Wang

2023

Advanced Science

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Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built‐in intelligence in their mechanical domain, the so‐called mechano‐intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision‐making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add‐on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high‐degree‐of‐freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self‐tuning wave controls to wave‐based logic gates. This research provides the much‐needed basis for creating future smart structures and materials that greatly surpass the state of the art—such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.

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