SNN-SC: A Spiking Semantic Communication Framework for Collaborative Intelligence
Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan

TL;DR
This paper introduces SNN-SC, a spiking neural network-based semantic communication framework for collaborative AI inference on edge devices, reducing transmission overhead and improving robustness over noisy channels.
Contribution
The paper proposes a novel SNN-based semantic communication model that directly transmits binary semantic features, enhancing efficiency and robustness compared to traditional DNN-based methods.
Findings
Higher compression ratio achieved.
Overcomes the cliff effect in noisy channels.
Lower computational complexity than DNN-based models.
Abstract
Collaborative Intelligence (CI) has emerged as a promising framework for deploying Artificial Intelligence (AI) models on resource-constrained edge devices. In CI, the AI model is partitioned between the edge device and the cloud, with intermediate features transmitted from the edge sub-model to the cloud sub-model to complete the inference task. However, reducing feature transmission overhead while maintaining task performance remains a challenge, particularly in the case of noisy wireless channels. In this paper, we propose a Spiking Neural Network (SNN)-based Semantic Communication (SC) model, SNN-SC, which extracts compact semantic information from features and transmits it through digital binary channels. Compared to the Deep Neural Network (DNN)-based SC model, whose output is floating-point, the binary output of SNN makes SNN-SC directly applicable to digital binary channels…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Molecular Communication and Nanonetworks
MethodsConvolution
