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...
By Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li, Tulika Mitra
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.
By Benjamin Karic, Nina Herrmann, Jan Stenkamp, Paula Scharf, Fabian Gieseke, Angela Schwering
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.
By Cristian Martella, Angelo Martella, Antonella Longo, Motaz Saad
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.
By Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr
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.
By \'Edouard Gu\'egain, Tristan Coignion
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).
By Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler