The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

Due to the advancements in sensing technologies and computational capabilities, system
health assessment and prognostics have been extensively investigated in the literature …

Today, convolutional neural networks (CNNs) are widely used in computer vision
applications. However, the trends of higher accuracy and higher resolution generate larger …

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional
deep learning frameworks, since they provide higher computational efficiency in event …

Many of today's deep neural network accelerators, eg, Google's TPU and NVIDIA's tensor
core, are built around accelerating the general matrix multiplication (ie, GEMM). However …

The Abstract is intended to provide a concise summary of the study and its scientific findings.
For AI/ML applications in medical physics, a problem statement and rationale for utilizing …

Transfer learning in natural language processing (NLP) uses increasingly large models that
tackle challenging problems. Consequently, these applications are driving the requirements …

GPUs accelerate high-throughput applications, which require orders-of-magnitude higher
memory bandwidth than traditional CPU-only systems. However, the capacity of such high …

We show in this work that memory intensive computations can result in severe performance
problems due to off-chip memory access and CPU-GPU context switch overheads in a wide …

Pipelining between data loading and computation is a critical tensor program optimization
for GPUs. In order to unleash the high performance of latest GPUs, we must perform a …