Autonomous driving platforms encounter diverse driving scenarios, each with varying hardware resources and precision requirements. Given the computational limitations of embedded devices, it is crucial to consider computing costs when deploying on …
The Large Language Models (LLMs) have been popular and widely used in creative ways because of their powerful capabilities. However, the substantial model size and complexity prevent LLMs from being implemented on resource constrained computing …
The convincing performances of deep neural networks (DNNs) can be degraded tremendously under malicious samples, known as adversarial examples. Besides, with the widespread edge platforms, it is essential to reduce the DNN model size for efficient …
The U.S. Department of Transportation's Inclusive Design Challenge spurred innovative research promoting accessible technology for people with disabilities in the future of autonomous transportation. This paper presents the user-driven design of the …
A power management framework for hybrid electric vehicles (HEVs) is proposed based on deep reinforcement learning (DRL) with a Long Short-Term Memory (LSTM) network to minimize the fuel consumption through determining the power distribution between …
Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning and inference. This article challenges the …
Training a Deep Neural Network (DNN) is a significant computing task since it places high demands on computing resources and memory bandwidth. Many approaches have been proposed to compress the network, while maintaining high model accuracy, reducing …
This paper investigates a service level agreements (SLAs)-based resource allocation problem in a server cluster. The objective is to maximise the total profit, which is the total revenue minus the operational cost of the server cluster. The total …
This paper studies the beamforming design problem of a multiuser downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is …