The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

Resource under-utilization is common in cloud data centers. Prior works have proposed
improving utilization by running provider workloads in the background, colocated with tenant …

Several fundamental changes in technology indicate domain-specific hardware and
software co-design is the only path left. In this context, architecture, system, data …

Workload characterization plays an important role in resource allocation and performance
optimization. In order to improve the resource utilization, cloud providers like Google and …

Deep Learning has recently been gaining popularity. From the micro-architecture field to the
upper-layer end applications, a lot of research work has been proposed in the literature to …

The digital revolution, rapidly decreasing storage cost, and remarkable results achieved by
state of the art machine learning (ML) methods are driving widespread adoption of ML …

In the last few years, the concept of data lake has become trendy for data storage and
analysis. Thus, several approaches have been proposed to build data lake systems …

Customization of a convolutional neural network (CNN) to a specific compute platform
involves finding an optimal pareto state between computational complexity of the CNN and …

For emerging datacenter (in short, DC) workloads, such as online Internet services or offline
data analytics, how to evaluate the upper bound performance and provide apple-to-apple …

Colocating workloads are commonly used in datacenters to improve server utilization.
However, the unpredictable application performance degradation caused by the contention …