Approximate multipliers (AMs) have widely been investigated to pursue high-performance
and energy-efficient hardware designs for error-tolerant applications, such as neural …

Leveraging approximate multipliers in approximate neural networks (ApproxNNs) can
effectively reduce hardware area and power consumption, making them suitable for edge …

In present scenario, significant data manipulations can be espied in image processing and
Convolutional Neural Networks (CNNs) applications. The quality performance of these …

Approximate multipliers are widely used in image processing and multimedia signal
processing applications for the reduction in area, computation time, and power consumption …

Deep Neural Network (DNN) hardware accelerators are essential in a spectrum of safety-
critical edge-AI applications with stringent reliability, energy efficiency, and latency …

The primary goal of approximate computing is enhancing system performance, such as
energy efficiency, speed, and form factor. Despite the growing use of approximate …

With the increasing complexity of digital systems, managing power dissipation and energy
consumption in digital circuits, particularly in emerging embedded systems for artificial …

Approximate multipliers enable hardware savings for error-resilient computation-intensive
applications. Most existing approximate multipliers have been on ASIC-based circuits. They …

The escalating computational demands of deep learning and large-scale models have led to
a significant increase in energy consumption, highlighting the urgent need for more energy …

To improve the performance and energy efficiency of the compute-intensive convolutional
neural networks (CNNs), approximate multipliers have widely been investigated, taking …