Experimental Demonstration of In-Memory Computing in a Ferrofluid System

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 x 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.

Automated summary

Magnetic fluids have potential in various research fields, but their computing capability has not been proven. This study demonstrates that a Fe3O4 water-based ferrofluid can perform electrical analogue computing and be programmed using quasi DC signals and read at RF mode. The fluid shows memristive behavior and is capable of classifying digits and conducting physical reservoir computing. This work opens up possibilities for in-memory computing and full-electric liquid computers using a fluid system.