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Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile ...
Robust Control of Quadruped Robots using Reinforcement Learning and Depth Completion Network
Achieving robust control of quadruped robots in dynamic and complex terrains is still a challenging task. Although reinforcement learning-based control strategies have made great progress in simulation and reality, motion control of quadruped robots ...
Enhancing Physical-Layer Key Generation Accuracy through Deep Learning-Based Hardware Calibration
This paper introduces a deep learning-based approach for calibrating hardware defects in physical-layer key generation (PKG) tasks, focusing on directional-of-arrival (DoA) based key generation in wireless communication systems. The proposed scheme ...
AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices
Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones, energy efficiency is critical for battery-powered devices. The rise of ...
Demo: Implementation and Benchmark of Magnetic Tracking on Mobile Platforms
Magnetic sensing is a promising approach for enabling high-accuracy and fine-grained tracking. Recent research has highlighted the superior performance of magnetic tracking in various subtle motion sensing tasks, such as hand and joint tracking. These ...