# On the Role of System Software in Energy Management of Neuromorphic Computing

arXiv: https://arxiv.org/abs/2103.12231 · HTML: https://mlsanity.com/paper/2103.12231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants](https://mlsanity.com/paper/2202.10570/) (2022)
- [Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training](https://mlsanity.com/paper/2205.13585/) (2022)

## Abstract (arXiv)

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.