This paper updates the survey of AI accelerators and processors from past three years. This
paper collects and summarizes the current commercial accelerators that have been publicly …

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic
resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for …

Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance
imaging that allows for efficient simultaneous measurements of multiple tissue properties …

Deep Learning has become a very promising avenue for magnetic resonance image (MRI)
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …

Tensor processing units (TPUs) are specialized hardware accelerators developed by
Google to support large-scale machine-learning tasks but they can also be leveraged to …

We demonstrate the use of Googles cloud-based Tensor Processing Units (TPUs) to
accelerate and scale up conventional (cubic-scaling) density functional theory (DFT) …

We have repurposed Google tensor processing units (TPUs), application-specific chips
developed for machine learning, into large-scale dense linear algebra supercomputers. The …

This paper presents a new approach which uses the tools within artificial intelligence (AI)
software libraries as an alternative way of solving partial differential equations (PDEs) that …

Matrix-multiplication units (MXUs) are now prevalent in every computing platform. The key
attribute that makes MXUs so successful is the semiring structure, which allows tiling for both …

Tensor Processing Units (TPUs) were developed by Google exclusively to support large-
scale machine learning tasks. TPUs can, however, also be used to accelerate and scale up …