Approximate computing has emerged as a new paradigm for high-performance and energy-
efficient design of circuits and systems. For the many approximate arithmetic circuits …

The logarithmic number system (LNS) has found appeal in digital arithmetic because it
allows multiplication and division to be performed much faster and more accurately than with …

In applications such as image processing and machine learning, imprecision can be
tolerated because of the nature of the application itself or the limitation of human senses. By …

Floating-point numbers represented using a hidden one can readily be approximately
converted to the logarithmic domain using Mitchell's approximation. Once in the logarithmic …

Neural networks (NNs) with large scales of artificial neurons are increasingly used in
recognition and classification tasks. In power-constrained scenarios, the tradeoff between …

Error resilient applications benefit from the use of approximate computing techniques that
enhance electrical performances while allowing a deviation from the exact result. Many …

Currently, deep learning is attracting massive researchers. As the scales of models and data
are increasing rapidly, the application-specific acceleration systems are emerging. With …

Most approximation computing research focuses on arithmetic operations like binary
multiplication, division, and addition. Squaring, another fundamental arithmetic operation, is …

Reducing power consumption in battery-powered medical edge devices is highly required.
This study aims to enhancing power efficiency in the Pan-Tompkins algorithm used for …

Square and square root functions are a fundamental step in many real-time digital signal
processing applications such as image processing, signal processing, and filtering. The …