Classical medium access control (MAC) protocols are interpretable, yet their
task-agnostic control signaling messages (CMs) are ill-suited for emerging
mission-critical applications. By contrast, neural network (NN) based protocol
models (NPMs) learn to generate task-specific CMs, but their rationale and
impact lack interpretability. To fill this void, in this article we propose,
for the first time, a semantic protocol model (SPM) constructed by transforming
an NPM into an interpretable symbolic graph written in the probabilistic logic
programming language (ProbLog). This transformation is viable by extracting and
merging common CMs and their connections while treating the NPM as a CM
generator. By extensive simulations, we corroborate that the SPM tightly
approximates its original NPM while occupying only 0.02% memory. By leveraging
its interpretability and memory-efficiency, we demonstrate several SPM-enabled
applications such as SPM reconfiguration for collision-avoidance, as well as
comparing different SPMs via semantic entropy calculation and storing multiple
SPMs to cope with non-stationary environments.

本文首次提出采用语义协议模型（SPM）将基于神经网络（NN）的协议模型（NPM）转换成可解释的符号图。该转换通过提取和合并通用控制信令消息（CM）和其连接而实现，从而具有内存效率和可解释性。该模型在多种实际应用中展示了性能和效率优势，如碰撞避免、语义熵计算和非静态环境下的存储管理。

面向语义通信协议的概率逻辑视角

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or Winograd-based algorithm. However, all these indirect methods
have high memory-overhead, which creates performance degradation and offers a
poor trade-off between performance and memory consumption. In this work, we
propose a memory-efficient convolution or MEC with compact lowering, which
reduces memory-overhead substantially and accelerates convolution process. MEC
lowers the input matrix in a simple yet efficient/compact way (i.e., much less
memory-overhead), and then executes multiple small matrix multiplications in
parallel to get convolution completed. Additionally, the reduced memory
footprint improves memory sub-system efficiency, improving performance. Our
experimental results show that MEC reduces memory consumption significantly
with good speedup on both mobile and server platforms, compared with other
indirect convolution algorithms.

本文提出一种具有内存效率的卷积方法，即 MEC，使用简单且高效 / 紧凑的方式降低输入矩阵的内存开销，提高卷积运算速度，显著减少内存消耗，在移动设备和服务器平台上都具有良好的加速效果，适用于卷积运算的深度神经网络模型。