The size and energy consumption of datacenters have been increasing significantly over the
past years. As a result, datacenters' increasing electricity monetary cost, energy …

Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

The Program Vulnerability Factor (PVF) has been proposed as a metric to understand the
impact of hardware faults on software. The PVF is calculated by identifying the program bits …

The current trend of ever-increasing performance in scientific applications comes with
tremendous growth in energy consumption. In this paper, we present GreenMM framework …

Renewable energy supply is a promising solution for datacenters' increasing electricity
monetary cost, energy consumption and harmful gas emissions. However, due to the …

Winograd convolution is originally proposed to reduce the computing overhead by
converting multiplication in neural network (NN) with addition via linear transformation. Other …

Linear algebra operations have been widely used in big data analytics and scientific
computations. Many works have been done on optimizing linear algebra operations on …

As we approach exascale computing, large-scale HPC systems are becoming increasingly
power-constrained, requiring them to run HPC workloads in an energy-efficient manner. The …

Emerging high-performance computing platforms, with large component counts and lower
power margins, are anticipated to be more susceptible to soft errors in both logic circuits and …

Power-consuming entities such as high performance computing (HPC) sites and large data
centers are growing with the advance in information technology. In business, HPC is used to …