It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Machine learning has enabled significant benefits in diverse fields, but, with a few
exceptions, has had limited impact on computer architecture. Recent work, however, has …

With the advent of mobile processors integrating CPU and GPU, high-performance tasks,
such as deep learning, gaming, and image processing are running on mobile devices. To …

As convolutional neural networks (CNNs) enable state-of-the-art computer vision
applications, their high energy consumption has emerged as a key impediment to their …

Recent breakthroughs in Machine Learning (ML) applications, and especially in Deep
Learning (DL), have made DL models a key component in almost every modern computing …

Increasing the number of cores integrated on a single chip offers a great potential for the
implementation of fault-tolerant techniques to achieve high reliability in real-time embedded …

Thermal optimization of a heterogeneous clustered multi-core processor under user-defined
QoS targets requires application migration and DVFS. However, selecting the core to …

Over the last two decades, as microprocessors have evolved to achieve higher
computational performance, their power density also has increased at an accelerated rate …

In the context of modern services that use multiple Deep Neural Networks (DNNs),
managing workloads on embedded devices presents unique challenges. These devices …

The problem of performance maximization under a thermal constraint has been tackled by
means of dynamic voltage and frequency scaling (DVFS) in many system-level optimization …