This paper explores the integration and application of Approximate Computing (AxC)
approaches to Machine Learning (ML), especially Deep Learning (DL) models. We focus on …

The design and implementation of Deep Learning (DL) models is currently receiving a lot of
attention from both industrials and academics. However, the computational workload …

Approximate computing (AxC) has been long accepted as a design alternative for efficient
system implementation at the cost of relaxed accuracy requirements. Despite the AxC …

Abstract Approximate Computing (AxC) paradigm aims at designing computing systems that
can satisfy the rising performance demands and improve the energy efficiency. AxC exploits …

A new design approach, called approximate computing (AxC), leverages the flexibility
provided by intrinsic application resilience to realize hardware or software implementations …

In this paper, we present our state-of-the-art approximate techniques that cover the main
pillars of approximate computing research. Our analysis considers both static and …

The rapid growth of demanding applications in domains applying multimedia processing
and machine learning has marked a new era for edge and cloud computing. These …

Despite the achieved speed-up in terms of memory and computation performances, the
workload involved in Deep Learning (DL) application is still hard to fit the embedded device …

For decades, the semiconductor industry enjoyed exponential improvements in
microprocessor power and performance with the device scaling of successive technology …

As the end of Dennard scaling looms, both the semiconductor industry and the research
community are exploring for innovative solutions that allow energy efficiency and …