In this paper, we propose a novel design for AI-native goal-oriented
communications, exploiting transformer neural networks under dynamic inference
constraints on bandwidth and computation. Transformers have become the standard
architecture for pretraining large-scale vision and text models, and
preliminary results have shown promising performance also in deep joint
source-channel coding (JSCC). Here, we consider a dynamic model where
communication happens over a channel with variable latency and bandwidth
constraints. Leveraging recent works on conditional computation, we exploit the
structure of the transformer blocks and the multihead attention operator to
design a trainable semantic token selection mechanism that learns to select
relevant tokens (e.g., image patches) from the input signal. This is done
dynamically, on a per-input basis, with a rate that can be chosen as an
additional input by the user. We show that our model improves over
state-of-the-art token selection mechanisms, exhibiting high accuracy for a
wide range of latency and bandwidth constraints, without the need for deploying
multiple architectures tailored to each constraint. Last, but not least, the
proposed token selection mechanism helps extract powerful semantics that are
easy to understand and explain, paving the way for interpretable-by-design
models for the next generation of AI-native communication systems.

我们提出了一种新颖的 AI 本地目标导向通信设计，利用变换器神经网络在带宽和计算的动态推理约束下。