The current Deep Learning (DL) landscape is fast-paced and is rife with non-uniform models, hardware/software (HW/SW) stacks, but lacks …
As Deep Learning (DL) models have been increasingly used in latency-sensitive applications, there has been a growing interest in …
There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application …
The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, …
The current landscape of cognitive pipelines exercises many Machine Learning (ML) and Deep Learning (DL) building blocks. These ML and …
Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as Tensor Core Units …
Data-intensive applications such as machine learning and analytics have created a demand for faster interconnects to avert the memory …
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative …
A major component of many advanced programming courses is an open-ended “end-of-term project” assignment. Delivering and evaluating …
Dynamic parallelism on GPUs simplifies the programming of many classes of applications that generate parallelizable work not known …
As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are …
As user demand scales for intelligent personal assistants (IPAs) such as Apple’s Siri, Google’s Google Now, and …