Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning.

ABSTRACT: In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memr

SUBMITTER: Wu X 

PROVIDER: S-EPMC9884246 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature