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Deep Learning Accelerators (DLAs) are effective to improve both performance and energy efficiency of compute-intensive deep learning algorithms. A flexible and portable mean to exploit DLAs is using high-performance software libraries with well-...
Recent advances in machine learning have leveraged dramatic increases in computational power, a trend expected to continue in the future. This paper introduces the first Hyperscale Hardware Optimized Neural Architecture Search (H2O-NAS) to ...
Cloud providers often have resources that are not being fully utilized, and they may offer them at a lower cost to make up for the reduced availability of these resources. However, customers may be hesitant to use such offerings (such as spot VMs) as ...
Support for Machine Learning (ML) applications in networking has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (including low-level languages to program them) presents a full-stack ...
Deformable Convolutional Networks (DCN) have been proposed as a powerful tool to boost the representation power of Convolutional Neural Networks (CNN) in computer vision tasks via adaptive sampling of the input feature map. Much like vision ...
Arm posture tracking is essential for many applications, such as gesture recognition, fitness training, and motion-based controls. Smartwatches with Inertial Measurement Unit (IMU) sensors (i.e., accelerometer, gyroscope, and magnetometer) provide a ...
Low-density parity-check (LDPC) codes have been widely used for Forward Error Correction (FEC) in wireless networks because they can approach the capacity of wireless links with lightweight encoding complexity. Although LoRa networks have been developed ...
Federated learning (FL) has attracted increasing attention as a promising technique to drive a vast number of edge devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of a FL system in practice due to the ...
Cyber-physical systems are starting to adopt neural network (NN) models for a variety of smart sensing applications. While several efforts seek better NN architectures for system performance improvement, few attempts have been made to study the ...
Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile ...
In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, ...
The integration of deep learning on Speaker Recognition (SR) advances its development and wide deployment, but also introduces the emerging threat of adversarial examples. However, only a few existing studies investigate its practical threat in physical ...
Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to ...
Tsetlin Machine (TM) is a new machine learning algorithm that encodes propositional logic into learning automata---a set of logical expressions composed of boolean input features---to recognise patterns. The simplicity, efficiency, and accuracy of this ...
Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we ...
Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared model without exposing their raw data. However, heterogeneous devices usually have limited and different available resources, i.e., system heterogeneity, for ...
Quality of Experience (QoE) assessment is a long-lasting but yet-to-be-resolved task. Existing approaches, especially for conversational voice services, are restricted to leveraging network-centric parameters. However, their performances are hardly ...
Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we find that GPU compute ...
Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple ...
In Internet of Things (IoT) scenarios such as smart homes, autonomous vehicles, and wearable devices, data pattern changes over time due to changing environments and user requirements, known as domain shifts. When encountering domain shifts, deep neural ...