  Fast model updates for unseen tasks on intelligent edge devices are crucial
but also challenging due to the limited computational power. In this paper,we
propose MetaLDC, which meta-trains braininspired ultra-efficient
low-dimensional computing classifiers to enable fast adaptation on tiny devices
with minimal computational costs. Concretely, during the meta-training stage,
MetaLDC meta trains a representation offline by explicitly taking into account
that the final (binary) class layer will be fine-tuned for fast adaptation for
unseen tasks on tiny devices; during the meta-testing stage, MetaLDC uses
closed-form gradients of the loss function to enable fast adaptation of the
class layer. Unlike traditional neural networks, MetaLDC is designed based on
the emerging LDC framework to enable ultra-efficient on-device inference. Our
experiments have demonstrated that compared to SOTA baselines, MetaLDC achieves
higher accuracy, robustness against random bit errors, as well as
cost-efficient hardware computation.
