相关论文: U-TOE: Universal TinyML On-board Evaluation Toolki…
Benchmarking AI systems in multi-turn interactive scenarios is essential for understanding their practical capabilities in real-world applications. However, existing evaluation protocols are highly heterogeneous, differing significantly in…
Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model conversion or complete reimplementation outside the PyTorch…
Recent advancements in machine learning (ML) have enabled its deployment on resource-constrained edge devices, fostering innovative applications such as intelligent environmental sensing. However, these devices, particularly…
Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focused on developing specific toolchains to support acceleration…
To meet next-generation IoT application demands, edge computing moves processing power and storage closer to the network edge to minimise latency and bandwidth utilisation. Edge computing is becoming popular as a result of these benefits,…
Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies. To address these challenges, this paper presents a novel,…
IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing…
Hardware-based Trusted Execution Environments (TEEs) are widely deployed in mobile devices. Yet their use has been limited primarily to applications developed by the device vendors. Recent standardization of TEE interfaces by GlobalPlatform…
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique…
Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models…
Recently, there has been a national push to use machine learning (ML) and artificial intelligence (AI) to advance engineering techniques in all disciplines ranging from advanced fracture mechanics in materials science to soil and water…
Profiling and minimizing the energy consumption of resource-constrained devices is an essential step towards employing IoT in various application domains. Due to the large size and high cost of commercial energy measurement platforms,…
This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of…
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy,…
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world through a diverse array of sensory channels. Commonly referred to as the `Internet of Things (IoT)' ecosystem, sensory data…
Earth observation (EO) missions traditionally rely on transmitting raw or minimally processed imagery from satellites to ground stations for computationally intensive analysis. This paradigm is infeasible for CubeSat systems due to…
The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the…
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to…
Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. We propose MCUNet, a framework that jointly…
Edge intelligence in IoT and IIoT demands lightweight algorithms for data processing on resource-constrained devices. This paper introduces a novel adaptive pulse shape filter based on TinyML for PAPR and SER optimization on edge devices…