  We propose semantic communication over wireless channels for various
modalities, e.g., text and images, in a task-oriented communications setup
where the task is classification. We present two approaches based on memory and
learning. Both approaches rely on a pre-trained neural network to extract
semantic information but differ in codebook construction. In the memory-based
approach, we use semantic quantization and compression models, leveraging past
source realizations as a codebook to eliminate the need for further training.
In the learning-based approach, we use a semantic vector quantized autoencoder
model that learns a codebook from scratch. Both are followed by a channel coder
in order to reliably convey semantic information to the receiver (classifier)
through the wireless medium. In addition to classification accuracy, we define
system time efficiency as a new performance metric. Our results demonstrate
that the proposed memory-based approach outperforms its learning-based
counterpart with respect to system time efficiency while offering comparable
accuracy to semantic agnostic conventional baselines.
