Near-Threshold Computing (NTC) has recently emerged as an attractive paradigm as it
allows devices to operate close to their optimal energy point (OEP). This work demonstrates …

Energy-aware architectures provide applications with a mix of low and high frequency cores.
Selecting the best core configurations for running programs is very challenging. Here, we …

Portable parallel programming models provide the potential for high performance and
productivity, however they come with a multitude of runtime parameters that can have …

In recent years we observe a rapid growth in the deployment of machine learning workloads
on big data analytics frameworks like Apache Spark and Apache Flink. These workloads are …

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 …

Dynamic Voltage/Frequency Scaling (DVFS) is a useful tool for improving system energy
efficiency, especially in multi-core chips where energy is more of a limiting factor. Per-core …

This position paper advocates a communications-inspired approach to the design of
machine learning systems on energy-constrained embeddedalways-on'platforms. The …

With the rise of the Internet of Things and Cyber Physical Systems with Artificial Intelligence,
it is important to design computational powerful and energy efficient embedded systems …

CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer
from inaccuracies due to the cost and complexity of setting their fine-grained parameters …

In performance critical applications, memory latency is frequently the dominant overhead. In
many cases, automatic compiler‐based optimizations to improve memory performance are …