  In Collaborative Intelligence (CI), Artificial Intelligence (AI) models are
split between edge devices and cloud. Features extracted from input on edge
devices are transmitted to the cloud for subsequent tasks. Extracting
task-related and compact information is critical when transmission bandwidth is
limited. In this paper, we propose a task-oriented Semantic Communication (SC)
framework (SNN-SC) to address this problem. In SC, only important information
for downstream tasks is extracted and transmitted. However, most of the
existing SC works only transmit analog information on the AWGN channel and
cannot be directly used for digital channels. SNN-SC fills this gap by using
Spiking Neural Networks (SNNs) to extract and transmit semantic information.
Since the outputs of SNNs are binary spikes, SNN-SC can be directly applied to
digital channels. In SNN-SC, a new spiking neuron is proposed to help the cloud
recover binary semantic information into informative floating-point features.
Furthermore, we improve the performance of SNN-SC by maximizing the entropy of
the semantic information. We evaluate the performance of SNN-SC on different
collaborative classification models, digital channels, and bandwidths. The
experimental results show that SNN-SC is more robust than the CNN-based SC
framework and separate source and channel coding method on digital channels.
