With the increasing computational demands of the neural networks, many hardware
accelerators for the neural networks have been proposed. Such existing neural network …

This paper proposes a novel approximate adder that exploits an error-reduced carry
prediction and constant truncation with error reduction schemes. The proposed adder …

Managing, diagnosis, prognosis, continuous monitoring, early detection, and preventing
chronic diseases for patients and elderly people have been gained a crucial role nowadays …

Approximate computing (AC) is an emerging computing paradigm suitable for intrinsic error-
tolerant applications to reduce energy consumption and execution time. Different …

This paper proposes six novel approximate 1-bit full adders (AFAs) for inexact computing.
The six novel AFAs namely AFA1, AFA2, AFA3, AFA4, AFA5, and AFA6 are derived from …

Low-power is a paramount concern in the design of 'digital signal processor'(DSP) for the
next generation portable electronic gadgets. The quest to achieve low-power with required …

This paper proposes new digital filter architecture based on a modified multiply-accumulate
(MAC) unit architecture called truncated MAC (TMAC), with the aim of increasing the …

Practical implementation of deep neural networks (DNNs) demands significant hardware
resources, necessitating high computational power and memory bandwidth. While existing …

Machine learning is widely used these days to extract meaningful information out of the
Zettabytes of sensors data collected daily. All applications require analyzing and …

Approximate computing (AC) is an emerging computing paradigm for energy efficiency. AC
is most suitable for error-tolerant applications, eg, image processing. The Sobel filter is an …