The paper surveys energy-aware TinyML research, mapping models, learning approaches, and energy-evaluation methods. It classifies influencing factors such as model architecture, hardware, communication, numerical precision, and input characteristics, and organizes optimization strategies into compression, hardware-aware design, system/communication, and data/processing categories. The study notes limited standardization in experimental platforms and reporting, hindering reproducibility and comparison.
arXiv:2608.21646v1 Announce Type: cross Abstract: TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper p...
The paper evaluates the energy consumption of sensor nodes in IoT applications, examining the entire pipeline from data acquisition to optional on-device CNN classification and LPWAN transmission using an ESP32‑S3 microcontroller. It compares on‑device inference followed by result transmission with raw data transmission across three environmental monitoring scenarios, varying model architecture and LPWAN technologies (LTE‑M, NB‑IoT, LoRaWAN). Results show that on‑device inference can reduce energy consumption by 1.7× to 6.9× compared to raw data transmission, with a tolerable loss in accuracy, and identify developer‑controllable variables that influence the energy trade‑off.
The paper introduces a cost‑aware hybrid approach that uses small, open‑source language models to classify smart data models (SDMs) in Internet of Things (IoT) environments. It benchmarks general‑purpose, reasoning‑specialized, and code‑specialized models across domain‑specific datasets, highlighting their suitability for edge devices with limited resources. The study also compares these lightweight models to large language models and near‑zero‑cost baselines such as TF‑IDF and a lightweight sentence encoder to demonstrate practical performance gains.
The paper presents a rapid pipeline for training and deploying machine‑learning models on the WeBe Band, a wrist‑worn wearable device. It automates the creation of hardware‑efficient models, integrates with the Piccolo AI ecosystem, and supports OTA deployment while profiling latency and memory usage. Experimental results show trade‑offs between classical models and lightweight neural networks for real‑time performance on a microcontroller.
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.
arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).
arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.
arXiv:2606. 24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.
arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.
arXiv:2512.08365v2 Announce Type: replace-cross Abstract: The training and deployment of machine learning (ML) models have become extremely energy-intensive. While existing optimization efforts focus...
The paper presents a novel multi‑exit computational scheme for TinyML on an ultra‑low‑power GAP9 SoC, adding confidence‑based gating points to a MobileNetV2 CNN for ImageNet‑100. By allowing inference to stop early, the approach cuts average MAC operations by 41 % (from 313 MMAC to 185 MMAC), reduces inference time by 29 % (49 ms to 35 ms), and saves 24 % in energy (2.1 mJ to 1.6 mJ per frame) with only a ~1 % drop in accuracy. Compared to a state‑of‑the‑art adaptive CNN on the same hardware, the method more than doubles computational efficiency, raising MAC/cycle from 8.1 to 17.2.