Given the stringent requirements of energy efficiency for Internet-of-Things edge devices,
approximate multipliers, as a basic component of many processors and accelerators, have …

A scalable approximate multiplier, called truncation-and rounding-based scalable
approximate multiplier (TOSAM) is presented, which reduces the number of partial products …

Artificial neural networks (NN) have shown a significant promise in difficult tasks like image
classification or speech recognition. Even well-optimized hardware implementations of …

In recent years, inexact computing has been increasingly regarded as one of the most
promising approaches for reducing energy consumption in many applications that can …

Approximate computing has emerged as a popular design paradigm for optimizing the
performance and energy consumption of error-resilient applications in domains such as …

To overcome the energy and bandwidth limitations of traditional IoT systems,“edge
computing” or information extraction at the sensor node has become popular. However, now …

In this paper, we demonstrate that disproportionate gains are possible through a simple
devise for injecting inexactness or approximation into the hardware architecture of a …

As pressures, notably from energy consumption, start impeding the growth and scale of
computing systems, inevitably, designers and users are increasingly considering the …

Inexact hardware design, which advocates trading the accuracy of computations in
exchange for significant savings in area, power and/or performance of computing hardware …

We propose a novel, error-efficient approximate multiplier (EEAM), which is based on a
rounding-based approach (RBA). Multiplication is performed using rounding, shift, and add …