The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is
challenging. Existing DNN frameworks and compilers often treat the DNN operators in a …

Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …

The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs
imply that better performance of CNN model inference on CPUs can deliver significant gain …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …

The DNN inferences are often batched for better utilizing the hardware in existing DNN
serving systems. However, DNN serving exhibits diversity in many aspects, such as input …

On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the
most common hardware for on-device inference and many inference frameworks have been …

In this paper, we present DynaGraph, a system that supports dynamic Graph Neural
Networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic …

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …