论文标题
时间编码数据的地址事件可变长度压缩
Address-Event Variable-Length Compression for Time-Encoded Data
论文作者
论文摘要
时间编码的信号，例如神经形态处理器中的社交网络更新日志和尖峰轨迹，是由多个痕迹在事件时间或尖峰时间内携带信息的多个痕迹来定义的。当相对于产生的位置在远程站点上处理时间编码的数据时，事件的发生需要及时编码和传输。神经形态芯片的标准地址事实表示（AER）方案（AER）协议编码在记录事件时产生的数据包的有效载荷中的“尖峰”痕迹的索引，因此在数据包的时机中隐含地编码事件的时间。本文调查了可以通过对数据包有效载荷进行可变的长度压缩来获得的潜在带宽节省。在社交网络或神经形态计算等应用中，压缩利用了跟踪和跟踪之间的相关性。该方法基于离散的时间鹰队过程和条件代码簿的熵编码。还提供了基于实际转发数据集的实验的结果。
Time-encoded signals, such as social network update logs and spiking traces in neuromorphic processors, are defined by multiple traces carrying information in the timing of events, or spikes. When time-encoded data is processed at a remote site with respect to the location it is produced, the occurrence of events needs to be encoded and transmitted in a timely fashion. The standard Address-Event Representation (AER) protocol for neuromorphic chips encodes the indices of the "spiking" traces in the payload of a packet produced at the same time the events are recorded, hence implicitly encoding the events' timing in the timing of the packet. This paper investigates the potential bandwidth saving that can be obtained by carrying out variable-length compression of packets' payloads. Compression leverages both intra-trace and inter-trace correlations over time that are typical in applications such as social networks or neuromorphic computing. The approach is based on discrete-time Hawkes processes and entropy coding with conditional codebooks. Results from an experiment based on a real-world retweet dataset are also provided.
