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Found 4 papers, 0 papers with code
Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator
As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs.
Automated Real Time Delineation of Supraclavicular Brachial Plexus in Neck Ultrasonography Videos: A Deep Learning Approach
Model was also tested for its ability to differentiate between supraclavicular and adjoining interscalene brachial plexus.
Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators
As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs.
DVIO: Depth aided visual inertial odometry for RGBD sensors
Specifically, we propose methods to use the depth measurement using one-dimensional (1D) feature parameterization as well as three-dimensional (3D) feature parameterization.
