  The rapidly evolving field of generative artificial intelligence technology
has introduced innovative approaches for developing semantic communication
(SemCom) frameworks, leading to the emergence of a new paradigm-generative
SemCom (GSC). However, the complex processes involved in semantic extraction
and generative inference may result in considerable latency in
resource-constrained scenarios. To tackle these issues, we introduce a new GSC
framework that involves fast and adaptive semantic transmission (FAST-GSC).
This framework incorporates one innovative communication mechanism and two
enhancement strategies at the transmitter and receiver, respectively. Aiming to
reduce task latency, our communication mechanism enables fast semantic
transmission by parallelizing the processes of semantic extraction at the
transmitter and inference at the receiver. Preliminary evaluations indicate
that while this mechanism effectively reduces task latency, it could
potentially compromise task performance. To address this issue, we propose two
additional methods for enhancement. First, at the transmitter, we employ
reinforcement learning to discern the intrinsic temporal dependencies among the
semantic units and design their extraction and transmission sequence
accordingly. Second, at the receiver, we design a semantic difference
calculation module and propose a sequential conditional denoising approach to
alleviate the stringent immediacy requirement for the reception of semantic
features. Extensive experiments demonstrate that our proposed architecture
achieves a performance score comparable to the conventional GSC architecture
while realizing a 52% reduction in residual task latency that extends beyond
the fixed inference duration.
