Power-Performance Characterization of TinyML Systems

2026-08-21

AI Summary

本文通过系统分析微控制器上TinyML应用的性能和功耗，提出模型估计不同抽象层的成本，并为优化边缘设备上的神经网络推理提供建议。

Abstract

TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performance and power characterization of diverse TinyML applications on microcontrollers (MCUs), spanning neural network models, software libraries, operating systems, and hardware architectures. We focus on the impact of the multiple layers of abstraction that provide higher programmability at the expense of performance and energy efficiency. We propose a model to estimate the costs of different abstraction layers and make recommendations for minimizing those costs. Our findings can help designers with Neural Architecture Search (NAS) and CNN inference optimization on edge devices.

Problem

• TinyML
• performance
• power characterization
• microcontrollers
• abstraction layers

Innovation

• TinyML
• Neural Architecture Search (NAS)
• performance and power characterization
• edge devices
• abstraction layers