The integration of AI tasks into Artificial Intelligence of Things (AIoT) devices has gained significant attention for its ability to reduce transmission latency and enhance data privacy compared to traditional cloud-based solutions. However, heterogeneous AIoT platforms face the challenge of balancing stringent latency constraints with energy efficiency under dynamic AI workloads. To address this issue, we propose BOLT-PM, a Bayesian Optimization framework for Latency-sensitive Task and Power Management, which dynamically allocates computing resources and optimizes power consumption in AIoT devices. We design a multi-branch neural network to accurately predict task execution times under varying resource constraints, enabling precise and adaptive performance estimation. We further enhance the Bayesian optimization process by embedding a variational autoencoder (VAE) to construct a smooth latent representation of the search space, thereby accelerating convergence and improving optimization stability. We implement BOLT-PM as a lightweight, platform-agnostic runtime framework that continuously adapts to workload fluctuations, maintaining latency guarantees while minimizing energy consumption. We evaluate BOLT-PM on several commercial AIoT boards, including RK3588, Jetson TX2, and Raspberry Pi, and compare it against classical heuristic methods and state-of-the-art approaches. Experimental results show that BOLT-PM achieves substantial energy savings while ensuring low-latency AI task execution, demonstrating its effectiveness as a robust and energy-efficient solution for power-aware AIoT applications.

Mobile edge computing is an emerging computing paradigm that enhances the computational capabilities of mobile devices by offloading intensive tasks to edge servers. In robotic systems, MEC can significantly reduce response times and improve user experience. However, as robots move through their environments, factors such as the distance to edge servers and physical obstructions fluctuate, leading to variations in communication bandwidth and, consequently, communication delays. To address these challenges, this paper proposes an adaptive computing node offloading framework (ARDF) designed to optimize the dynamic deployment of Robot Operating System 2 (ROS2) nodes in robot-edge environments. The framework enables developers to flexibly deploy robotic computing tasks based on varying computational and network conditions. We validate its effectiveness through experiments involving robotic arm control, 3D detection applications, and numerically simulated ROS2 tasks under different bandwidth conditions. The results demonstrate that the framework significantly improves response times for ROS2 applications, even under fluctuating computational loads and network constraints. The code for the framework ARDF can be found on GitHub1.

ABSTRACT Scheduling applications in vehicular edge computing (VEC) systems poses significant challenges due to strict timing constraints and varying levels of criticality. This paper presents a three‐stage scheduling framework designed to efficiently manage the execution of mixed‐criticality applications. The proposed method introduces scheduling policies that reduce the complexity of scheduling dual‐criticality DAG (Directed Acyclic Graph) applications on servers by transforming them into equivalent uniprocessor scheduling problems. To further enhance performance, a population‐based evolutionary algorithm is employed to optimize virtual machine configurations on each server, while a game‐theoretic approach assigns DAG applications to servers. Experimental results show that the proposed scheme outperforms both state‐of‐the‐art dynamic programming (DP) and particle swarm optimization (PSO) methods. The proposed MCS approach achieves a strong balance between scheduling quality and computational efficiency, with an of 0.87, an 80% success rate, and a low computation time (310 s), making it well‐suited for real‐time edge systems. Compared to other methods like PSO+, DP, and OneVM, MCS offers near‐optimal performance while avoiding the high computational cost and scalability limitations faced by those alternatives.

Deploying deep neural networks (DNNs) on resource-constrained Internet of Things (IoT) devices is challenging due to limited processing power, energy constraints, and stringent latency requirements. This article introduces a new method to split DNN tasks in IoT systems, focusing on saving power and reducing delays. We use Gaussian process regression (GPR) to predict how long tasks will take under different conditions. We also use a simple linear regression model to estimate how much power IoT devices use based on their CPU usage. These predictive models are integrated into a Bayesian optimization framework to determine the optimal DNN task partitioning point, balancing latency and energy efficiency. The system adjusts to changes in the network and device conditions, ensuring it works well in different situations. Experiments on a heterogeneous IoT testbed demonstrate that GPR accurately predicts execution latencies, and the linear regression model provides reliable power consumption estimates. The Bayesian optimization algorithm efficiently explores the tradeoff space, offering low power consumption and high latency satisfaction rates. Our code is shared for public usehttps://github.com/nucleusbiao/Time-Sensitive-and-Power-Efficient-DNN-Task-Partitioning.

Few-shot learning aims to develop models with strong generalization capabilities using a small number of training samples. However, most learning methods rely solely on the visual features of a few samples to represent entire categories, leading to poor category representativeness. In contrast, humans can utilize multimodal information to learn category features, thereby making them more representative. Hence, this article emulates the human multimodal learning mechanism by integrating visual features with textual information, thereby facilitating the model's acquisition of more representative and robust category features. Specifically, this article introduces a novel multimodal fusion mechanism-the visual-semantic fusion selection mechanism (VSFSM)-which comprises a fusion selection module (FS-Module) and a category enhancement module (CE-Module). These two modules collaboratively enhance the model's classification performance. The FS-Module aligns and fuses semantic information with visual features across both channel and spatial dimensions, performing feature selection and reconstruction. This process not only generates representative category features but also mitigates the impact of noise. The CE-Module guides the model to emphasize category-specific features in the query images, ultimately yielding representative visual-semantic category features while reducing the interference of noise in the query images. Additionally, to better facilitate few-shot learning, this article introduces a novel objective loss function for optimized training. Extensive comparative and ablation experiments conducted on multiple datasets further validate the effectiveness of the proposed method.

Tagged geometric history length (TAGE) branch predictor shows good prediction accuracy with sufficient storage budget. However, shrinking TAGE's storage budget dramatically reduces its prediction accuracy. In this paper, we propose MT (Misprediction Table)-TAGE, which adds a side predictor to the TAGE branch predictor. This side branch predictor will record the branches with the highest misprediction rate during the running process of the program and modify the prediction result of the branch when the number of mispredictions is higher than dynamic threshold. We also propose a new algorithm that enables MT-TAGE to be implemented in multithreaded processors. This work is written in SystemVerilog and tested on a RISC-V multithreaded processor. The experimental results show that in a quad-thread processor, the misprediction rates of MT-TAGE under the storage budget of 32kbits and 8kbits are 5.03MPKI (misprediction per kilo-instructions) and 5.54MPKI, respectively, which are 12.5% and 15.5% less than TAGE under the same area.

In wireless power and data transfer (WPDT) systems implemented with amplitude shift keying (ASK) data demodulation, the low amplitude modulation depth (MD) is usually preferred as it helps to improve energy harvesting efficiency, transmission range and stability. In this paper, a fully integrated ASK demodulator supporting ultra-low MD is proposed, which comprises a two-stage self-biased shifted limiter (SSL) that provides sufficient conversion gain and operates at low power consumption by introducing an adaptive biasing circuit. This structure is implemented in 0.18$\mu \mathbf{m}$high-voltage Bipolar-CMOS-DMOS technology. The detectable MD is measured as low as 0.03%, while the power consumption is only 52.5$\mu \mathbf{W}$.

Due to the flexibility, availability, and scalability of cloud computing services, more and more users seek solutions via cloud computing techniques. A cloud computing platform often consists of a large number of infrastructures, and its energy consumption is a big problem. In this article, we study how to minimize the energy consumption of a cloud computing platform when handling some intermittent real-time tasks. Unlike previous works that abstract users’ submitted tasks as single computation jobs and process them using CPU, this work proposes using CPU and GPU to process intermittent real-time tasks that occur at irregular intervals and their released computation jobs must be completed within required time limits. The energy consumption minimization problem is formulated as an integer nonlinear programming problem that needs to decide on a task assignment plan and a specific resource allocation plan. To effectively solve this problem, we define a state that represents the optimal solution for a given set of tasks with a given amount of resources, as well as a value function that represents the value of a state. In this way, we derive a state-transition equation and develop a dynamic programming method to solve the problem. This method is also extended to handle tasks whose arrival time is dynamic and unpredictable. Experiments show that the proposed algorithm can effectively reduce energy consumption, while its computation time is quite low compared to some other greedy methods.