This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep
neural network (DNN) signal processing, with particular reference to assisted-and …

The slowdown of Moore's law and the power wall necessitates a shift toward finely tunable
precision (aka transprecision) computing to reduce energy footprint. Hence, we need circuits …

Hardware implementation of Floating Point Units (FPUs) has been a key area of research
due to their massive area and energy footprints. Recently, a proposal was made to replace …

This paper explores the potential of Lagrangian duality for learning applications that feature
complex constraints. Such constraints arise in many science and engineering domains …

High performance and, at the same time, energy efficiency are important yet often conflicting
requirements in many fields of emerging applications. Those applications range from multi …

Transprecision computing targets energy-efficiency with multiple floating-point modules with
different precisions to suit application requirements. Variable-precision architectures aim at …

This paper aims to take stock of recent advances in the field of energy-quality (EQ) scalable
circuits and systems, as promising direction to continue the historical exponential energy …

Text summarization in the biomedical domain has largely been limited to extractive
approaches. Abstractive approaches, using deep learning, have recently been successful …

In recent years approximate computing has been extensively explored as a paradigm to
design hardware and software solutions that save energy by trading off on the quality of the …

With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs)
by real-time scenarios, there is the need to review information representation. A very …