Neuromorphic architectures implement biological neurons and synapses to execute machine learning algorithms with spiking neurons and bio-inspired learning algorithms. These architectures are energy efficient and therefore, suitable for cognitive information processing on resource and power-constrained environments, ones where sensor and edge nodes of internet-of-things (IoT) operate. To map a spiking neural network (SNN) to a neuromorphic architecture, prior works have proposed design-time based solutions, where the SNN is first analyzed offline using representative data and then mapped to the hardware to optimize some objective functions such as minimizing spike communication or maximizing resource utilization. In many emerging applications, machine learning models may change based on the input using some online learning rules. In online learning, new connections may form or existing connections may disappear at run-time based on input excitation. Therefore, an already mapped SNN may need to be re-mapped to the neuromorphic hardware to ensure optimal performance. Unfortunately, due to the high computation time, design-time based approaches are not suitable for remapping a machine learning model at run-time after every learning epoch. In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at run-time. Our design methodology operates in two steps – step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture. We conduct experiments to evaluate the feasibility of our algorithm using synthetic and realistic SNN-based applications. We demonstrate that our algorithm reduces SNN mapping time by an average 780x compared to a state-of-the-art design-time based SNN partitioning approach with only 6.25% lower solution quality.

神经形态架构实现生物神经元和突触来执行带有尖峰神经元和生物启发性学习算法的机器学习算法。这些架构具有高能效，因此适合在资源和功率受限的环境（物联网（IoT）的传感器和边缘节点运行）的环境中进行认知信息处理。为了将尖峰神经网络（SNN）映射到神经形态架构，先前的工作提出了基于设计时的解决方案，其中先使用代表性数据对SNN进行脱机分析，然后将其映射到硬件上以优化一些目标功能，例如最小化尖峰通信或最大限度地利用资源。在许多新兴的应用程序中，机器学习模型可能会使用一些在线学习规则基于输入进行更改。在在线学习中 基于输入激励，可能会在运行时形成新的连接或现有的连接消失。因此，可能需要将已经映射的SNN重新映射到神经形态硬件，以确保最佳性能。不幸的是，由于计算时间长，基于设计时的方法不适合在每个学习时期之后在运行时重新映射机器学习模型。在本文中，我们提出了一种设计方法，用于在运行时将基于在线学习SNN的应用程序的神经元和突触分区和映射到神经形态架构。我们的设计方法分两步进行：第1步是一种分层贪婪方法，将SNN分为神经元和突触的簇，并结合了神经形态结构的约束，步骤2是爬山优化算法，该算法可最大程度地减少群集之间传递的总尖峰，从而改善架构共享互连上的能耗。我们进行实验，以使用基于合成和现实SNN的应用程序评估我们算法的可行性。我们证明，与基于最新设计时间的SNN分区方法相比，我们的算法将SNN映射时间平均缩短了780倍，而解决方案质量仅降低了6.25％。