Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware

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.