Experimental Demonstration of In‐Memory Computing in a Ferrofluid System

• Published in: Advanced materials (Weinheim) Vol. 35; no. 23; pp. e2211406 - n/a
• Main Authors: Crepaldi, Marco, Mohan, Charanraj, Garofalo, Erik, Adamatzky, Andrew, Szaciłowski, Konrad, Chiolerio, Alessandro
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.06.2023
• Subjects: Biomedical materials; Computation; Direct current; Energy harvesting; ferrofluid; Ferrofluids; Information storage; in‐memory computing; Iron oxides; Magnetic fluids; Materials science; memristive liquid; Radio frequency; Robotics; Signal processing; Storage capacity; in-memory computing; memristive liquid; ferrofluid
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202211406

Magnetic fluids are excellent candidates for several important research fields including energy harvesting, biomedical applications, soft robotics, and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computing capability, including the emulation of synaptic functions, which requires complex non‐linear dynamics. Here, it is experimentally demonstrated that a Fe3O4 water‐based ferrofluid (FF) can perform electrical analogue computing and be programmed using quasi direct current (DC) signals and read at radio frequency (RF) mode. Features have been observed in all respects attributable to a memristive behavior, featuring both short and long‐term information storage capacity and plasticity. The colloid is capable of classifying digits of a 8 × 8 pixel dataset using a custom in‐memory signal processing scheme, and through physical reservoir computing by training a readout layer. These findings demonstrate the feasibility of in‐memory computing using an amorphous FF system in a liquid aggregation state. This work poses the basis for the exploitation of a FF colloid as both an in‐memory computing device and as a full‐electric liquid computer thanks to its fluidity and the reported complex dynamics, via probing read‐out and programming ports. A water‐based ferrofluid system can implement a liquid in‐memory computer when it is stimulated in direct current and its internal state is read in radio frequency mode. The liquid can recognize digits using either a custom pixel serialization scheme or reservoir computing with an ad‐hoc read‐out neural network layer.