Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based. However, they have trouble decoding temporal information with high accuracy. Thus, they commonly resort to recurrence or delays to enhance their temporal computing ability which, however, bring downsides in terms of hardware-efficiency. In the brain, dendrites are computational powerhouses that just recently started to be acknowledged in such machine learning systems. In this work, we focus on a sequence detection mechanism present in branches of dendrites and translate it into a novel type of neural network by introducing a dendrocentric neural network, DendroNN. DendroNNs identify unique incoming spike sequences as spatiotemporal features. This work further introduces a rewiring phase to train the non-differentiable spike sequences without the use of gradients. During the rewiring, the network memorizes frequently occurring sequences and additionally discards those that do not contribute any discriminative information. The networks display competitive accuracies across various event-based time series datasets. We also propose an asynchronous digital hardware architecture using a time-wheel mechanism that builds on the event-driven design of DendroNNs, eliminating per-step global updates typical of delay- or recurrence-based models. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at comparable accuracy on the same audio classification task, demonstrating its suitability for spatiotemporal event-based computing. This work offers a novel approach to low-power spatiotemporal processing on event-driven hardware.

翻译：时空信息是多种感官处理与计算任务的核心。前馈脉冲神经网络可用于解决此类任务，同时通过基于事件的计算在能效方面具有潜在优势。然而，这类网络难以高精度解码时间信息。因此，它们通常借助循环连接或延迟机制来增强时间计算能力，但这会降低硬件效率。在大脑中，树突是最近才开始在此类机器学习系统中得到认可的计算核心部件。本研究聚焦于树突分支中存在的序列检测机制，通过引入一种树突中心神经网络DendroNN，将其转化为新型神经网络架构。DendroNN能够将独特的输入脉冲序列识别为时空特征。本文进一步引入重连训练阶段，无需梯度即可训练不可微分的脉冲序列。在重连过程中，网络会记忆频繁出现的序列，并剔除那些不提供判别性信息的序列。该网络在多种基于事件的时间序列数据集上展现出具有竞争力的准确率。我们还提出了一种采用时间轮机制的异步数字硬件架构，该架构基于DendroNN的事件驱动设计，消除了基于延迟或循环的模型中典型的每步全局更新。通过利用DendroNN的动态与静态稀疏性以及内在量化特性，该架构在相同音频分类任务中，以相当精度实现了比最先进神经形态硬件高达4倍的能效提升，证明了其在时空事件驱动计算中的适用性。本研究为事件驱动硬件上的低功耗时空处理提供了创新方法。