Energy Aware Development of Neuromorphic Implantables: From Metrics to Action

TL;DR

This paper explores the current state of energy efficiency metrics for neuromorphic SNNs, identifying gaps in practicality and fidelity, and proposes future research directions to improve actionable energy assessment tools for implantable devices.

Contribution

It classifies existing energy metrics for SNNs, evaluates their usefulness, and outlines new research directions to develop more practical and actionable energy efficiency metrics.

Findings

Many metrics lack practical insights for developers.

A gap exists between accessible and high-fidelity metrics.

Future metrics should include trend-based and battery-aware assessments.

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.