论文标题
尖峰神经网络与神经形态硬件的运行时间映射
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
论文作者
论文摘要
在本文中，我们提出了一种设计方法，以在{Run Time}处将基于SNN的应用程序划分和映射基于SNN的应用程序的神经元和突触。我们的设计方法分为两个步骤 - 步骤1是将SNN划分为神经元和突触群中的一种谨慎的方法，结合了神经形态结构的约束，步骤2是一种爬山的优化算法，可将架构之间的总跨度降至最低，从而改善了共享互联架上的能量消费。我们进行实验，以使用基于SNN的应用程序来评估算法的可行性。我们证明，与最先进的基于设计的SNN分区方法相比，我们的算法将SNN映射时间平均减少了780倍，仅溶液质量降低了6.25 \％。
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.
