Spiking neural network (SNN), as a brain-inspired energy-efficient neural
network, has attracted the interest of researchers. While the training of
spiking neural networks is still an open problem. One effective way is to map
the weight of trained ANN to SNN to achieve high reasoning ability. However,
the converted spiking neural network often suffers from performance degradation
and a considerable time delay. To speed up the inference process and obtain
higher accuracy, we theoretically analyze the errors in the conversion process
from three perspectives: the differences between IF and ReLU, time dimension,
and pooling operation. We propose a neuron model for releasing burst spikes, a
cheap but highly efficient method to solve residual information. In addition,
Lateral Inhibition Pooling (LIPooling) is proposed to solve the inaccuracy
problem caused by MaxPooling in the conversion process. Experimental results on
CIFAR and ImageNet demonstrate that our algorithm is efficient and accurate.
For example, our method can ensure nearly lossless conversion of SNN and only
use about 1/10 (less than 100) simulation time under 0.693$\times$ energy
consumption of the typical method. Our code is available at
this https URL.

探讨了 ANN 转换成 SNN 的过程中容易出现的问题，并提出了利用 Lateral Inhibition Pooling 和 Burst spikes 等方法加快推理过程并提高准确性的算法，在 CIFAR 和 ImageNet 数据集上实验取得了很高的效率和精度。

具有突发电位的脉冲神经网络的高效准确转换

Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes

DBpedia is one of the first and most prominent nodes of the Linked Open Data
cloud. It provides structured data for more than 100 Wikipedia language
editions as well as Wikimedia Commons, has a mature ontology and a stable and
thorough Linked Data publishing lifecycle. Wikidata, on the other hand, has
recently emerged as a user curated source for structured information which is
included in Wikipedia. In this paper, we present how Wikidata is incorporated
in the DBpedia ecosystem. Enriching DBpedia with structured information from
Wikidata provides added value for a number of usage scenarios. We outline those
scenarios and describe the structure and conversion process of the
DBpediaWikidata dataset.

介绍了如何将 Wikidata 融入 DBpedia 生态系统并描述了这一数据集的结构和转换过程。