With the proliferation of connected devices including smartphones, novel network
connectivity and management methods are needed to meet user Quality of Experience …

Deep learning on point clouds has attracted increasing attention for various emerging 3D
computer vision applications, such as autonomous driving, robotics, and virtual reality …

Deploying deep neural networks (DNNs) for a resource-constrained environment and
achieving satisfactory performance is challenging. It is especially so on microcontrollers for …

Continuous Vision (CV) systems are essential for emerging applications like Autonomous
Driving (AD) and Augmented/Virtual Reality (AR/VR). A standard CV System-on-a-Chip …

Machine learning (ML) on resource-constrained edge devices is expensive and often
requires offloading computation to the cloud, which may compromise the privacy of user …

Memory bandwidth utilization has become the key performance bottleneck for state-of-the-
art variants of neural network kernels. Current structures such as depth-wise, point-wise and …

Parameter sensitivity analysis (SA) is an effective tool to gain knowledge about complex
analysis applications and assess the variability in their analysis results. However, it is an …

Edge devices like smartphones, wearables, and personal assistants have become an
integral part of our daily routines. Their ubiquitous and portable nature allows them to …

CNN-based stereo matching methods achieve great performance but come with high
computational requirements. Pruning a CNN can reduce the complexity but may in turn lead …