  Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy
efficiency of machine learning programs, especially those that are implemented
using Spiking Neural Network (SNN). The role of a system software for
neuromorphic systems is to cluster a large machine learning model (e.g., with
many neurons and synapses) and map these clusters to the computing resources of
the hardware. In this work, we formulate the energy consumption of a
neuromorphic hardware, considering the power consumed by neurons and synapses,
and the energy consumed in communicating spikes on the interconnect. Based on
such formulation, we first evaluate the role of a system software in managing
the energy consumption of neuromorphic systems. Next, we formulate a simple
heuristic-based mapping approach to place the neurons and synapses onto the
computing resources to reduce energy consumption. We evaluate our approach with
10 machine learning applications and demonstrate that the proposed mapping
approach leads to a significant reduction of energy consumption of neuromorphic
computing systems.
