Address-Event-Representation (AER) is a spike-routing protocol that allows the scaling of neuromorphic and spiking neural network (SNN) architectures. However, in conventional neuromorphic architectures, the AER protocol and in general, any virtual interconnect plays only a passive role in computation, i.e., only for routing spikes and events. In this paper, we show how causal temporal primitives like delay, triggering, and sorting inherent in the AER protocol itself can be exploited for scalable neuromorphic computing using our proposed technique called Time-to-Event Margin Propagation (TEMP). The proposed TEMP-based AER architecture is fully asynchronous and relies on interconnect delays for memory and computing as opposed to conventional and local multiply-and-accumulate (MAC) operations. We show that the time-based encoding in the TEMP neural network produces a spatio-temporal representation that can encode a large number of discriminatory patterns. As a proof-of-concept, we show that a trained TEMP-based convolutional neural network (CNN) can demonstrate an accuracy greater than 99% on the MNIST dataset and 91.2% on the Fashion MNIST Dataset. Overall, our work is a biologically inspired computing paradigm that brings forth a new dimension of research to the field of neuromorphic computing.

中文翻译：

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使用事件时间裕度传播进行地址事件表示的神经形态计算

地址事件表示 (AER) 是一种尖峰路由协议，允许扩展神经形态和尖峰神经网络 (SNN) 架构。然而，在传统的神经形态架构、AER 协议以及一般情况下，任何虚拟互连在计算中仅扮演被动角色，即仅用于路由尖峰和事件。在本文中，我们展示了如何使用我们提出的称为事件时间裕度传播（TEMP）的技术，利用 AER 协议本身固有的延迟、触发和排序等因果时间原语来进行可扩展的神经形态计算。所提出的基于 TEMP 的 AER 架构是完全异步的，并且依赖于内存和计算的互连延迟，而不是传统的本地乘法累加 (MAC) 操作。我们表明，TEMP 神经网络中基于时间的编码产生了一种时空表示，可以编码大量的歧视模式。作为概念验证，我们证明经过训练的基于 TEMP 的卷积神经网络 (CNN) 在 MNIST 数据集上的准确率高于 99%，在时尚 MNIST 数据集上的准确率高于 91.2%。总的来说，我们的工作是一种受生物学启发的计​​算范式，为神经形态计算领域的研究带来了新的维度。