Address-Event-Representation (AER) is a spike-routing protocol that allows the scaling of neuromorphic and spiking neural network (SNN) architectures to a size that is comparable to that of digital neural network 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. Overall, our work is a biologically inspired computing paradigm that brings forth a new dimension of research to the field of neuromorphic computing.

翻译：地址-事件表示（AER）是一种脉冲路由协议，使得神经形态和脉冲神经网络（SNN）架构能够扩展到与数字神经网络架构相当的规模。然而，在传统神经形态架构中，AER协议以及一般意义上的任何虚拟互连仅在计算中扮演被动角色，即仅用于路由脉冲和事件。本文展示了如何利用AER协议本身固有的因果时间基元（如延迟、触发和排序），通过我们提出的技术——时间-事件裕度传播（TEMP），实现可扩展的神经形态计算。所提出的基于TEMP的AER架构完全异步，并依赖互连延迟进行存储和计算，而非传统的局部乘累加（MAC）操作。我们证明，TEMP神经网络中的基于时间的编码能够产生空间-时间表征，编码大量区分性模式。作为概念验证，我们表明，在MNIST数据集上，训练后的基于TEMP的卷积神经网络（CNN）可实现超过99%的准确率。总体而言，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开辟了新的研究方向。