Energy Aware Development of Neuromorphic Implantables: From Metrics to Action

Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary exploratory study of energy efficiency metrics proposed in the SNN benchmarking literature. We classify 13 commonly used metrics based on four key properties: Accessibility, Fidelity, Actionability, and Trend-Based analysis. Our findings indicate that while many existing metrics provide useful comparisons between architectures, they often lack practical insights for SNN developers. Notably, we identify a gap between accessible and high-fidelity metrics, limiting early-stage energy assessment. Additionally, we emphasize the lack of metrics that provide practitioners with actionable insights, making it difficult to guide energy-efficient SNN development. To address these challenges, we outline research directions for bridging accessibility and fidelity and finding new Actionable metrics for implantable neuromorphic devices, introducing more Trend-Based metrics, metrics that reflect changes in power requirements, battery-aware metrics, and improving energy-performance tradeoff assessments. The results from this paper pave the way for future research on enhancing energy metrics and their Actionability for SNNs.

Pre-print

          @inproceedings{DBLP:conf/ict4s/RoqueC25,
author       = {Enrique Barba Roque and
                Luis Cruz},
title        = {Energy Aware Development of Neuromorphic Implantables: From Metrics
                to Action},
booktitle    = {11th International Conference on {ICT} for Sustainability, {ICT4S}
                2025, Dublin, Ireland, June 9-13, 2025},
pages        = {198--208},
publisher    = {{IEEE}},
year         = {2025},
url          = {https://doi.org/10.1109/ICT4S68164.2025.00028},
doi          = {10.1109/ICT4S68164.2025.00028},
timestamp    = {Fri, 31 Oct 2025 15:32:54 +0100},
biburl       = {https://dblp.org/rec/conf/ict4s/RoqueC25.bib},
bibsource    = {dblp computer science bibliography, https://dblp.org}
}