Energy Aware Development of Neuromorphic Implantables: From Metrics to Action

• URL: http://arxiv.org/abs/2506.09599v1
• Date: Wed, 11 Jun 2025 10:58:36 GMT
• Title: Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
• Authors: Enrique Barba Roque, Luis Cruz,
• Abstract summary: Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs)<n>Assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics.
• Score: 5.4453305205374445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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