Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems

2026-02-19

AI Summary

This work addresses the multi-objective optimization challenge of deploying AI models on ARM Cortex-M embedded processors, balancing energy efficiency, accuracy, and resource utilization. The authors propose an automated, Pareto-optimal multi-objective benchmarking framework to systematically evaluate key performance metrics across Cortex-M0+, M4, and M7 cores. Their analysis reveals an approximately linear relationship between FLOPs and inference latency and quantifies the trade-off between energy consumption and model accuracy through Pareto front characterization. The study demonstrates that the M7 excels in short inference tasks, the M4 achieves superior energy efficiency for longer workloads, and the M0+ is best suited for lightweight applications, thereby offering clear guidance for processor selection and sustainable design in embedded AI systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This work presents a practical benchmarking framework for optimizing artificial intelligence (AI) models on ARM Cortex processors (M0+, M4, M7), focusing on energy efficiency, accuracy, and resource utilization in embedded systems. Through the design of an automated test bench, we provide a systematic approach to evaluate across key performance indicators (KPIs) and identify optimal combinations of processor and AI model. The research highlights a nearlinear correlation between floating-point operations (FLOPs) and inference time, offering a reliable metric for estimating computational demands. Using Pareto analysis, we demonstrate how to balance trade-offs between energy consumption and model accuracy, ensuring that AI applications meet performance requirements without compromising sustainability. Key findings indicate that the M7 processor is ideal for short inference cycles, while the M4 processor offers better energy efficiency for longer inference tasks. The M0+ processor, while less efficient for complex AI models, remains suitable for simpler tasks. This work provides insights for developers, guiding them to design energy-efficient AI systems that deliver high performance in realworld applications.

Problem

• Pareto optimality
• AI benchmarking
• ARM Cortex processors
• energy efficiency
• embedded systems

Innovation

• Pareto optimization
• ARM Cortex benchmarking
• energy-efficient AI
• embedded AI systems
• FLOPs-inference correlation