Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The market for remote sensing space-based applications is fundamentally limited by up-and
downlink bandwidth and onboard compute capability for space data handling systems. This …

New machine learning accelerators are being announced and released each month for a
variety of applications from speech recognition, video object detection, assisted driving, and …

Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …

Over the past several years, new machine learning accelerators were being announced and
released every month for a variety of applications from speech recognition, video object …

Attention-based models have demonstrated remarkable success in various natural
language understanding tasks. However, efficient execution remains a challenge for these …

DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

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 …

Although running deep-learning (DL) algorithms is challenging due to resource constraints
on mobile and wearable devices, they provide performance improvements compared to …

Stochastic rounding (SR) randomly maps a real number x to one of the two nearest values in
a finite precision number system. The probability of choosing either of these two numbers is …