Multi-modal computing (M 2 C) has recently exhibited impressive accuracy improvements in
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …

GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …

Energy conservation of large data centers for high performance computing workloads, such
as deep learning with Big Data, is of critical significance, where cutting down a few percent …

In the quest for exascale computing, energy-efficiency is a fundamental goal in high-
performance computing systems, typically achieved via dynamic voltage and frequency …

Increased reliance on graphics processing units (GPUs) for high-intensity computing tasks
raises challenges regarding energy consumption. To address this issue, dynamic voltage …

Energy efficiency will be important in future accelerator-based HPC systems for
sustainability and to improve overall performance. This study proposes a deep neural …

The parallel advancement of AI and IoT technologies has recently boosted the development
of multi-modal computing (M 2 C) on pervasive autonomous embedded systems (AES). M 2 …

The increasing application of Machine Learning (ML) techniques on the Internet of Things
(IoTs) has led to the leverage of ML accelerators like General Purpose Computing on …

The emergence of Machine Learning (ML) as a powerful technique has been helping nearly
all fields of business to increase operational efficiency or to develop new value propositions …

Power consumption is one of the top limiting factors in high-performance computing systems
and data centers, and dynamic voltage and frequency scaling (DVFS) is an important …