Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

Memristive crossbar architectures are evolving as powerful in-memory computing engines
for artificial neural networks. However, the limited number of non-volatile conductance states …

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 …

The challenging deployment of compute-intensive applications from domains such Artificial
Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing …

Approximate computing (AxC) is increasingly emerging as a new design paradigm to
produce more efficient computation systems by judiciously reducing the computation quality …

Approximate computing is an efficient approach to reduce the design complexity for error-
resilient applications. Multipliers are key arithmetic units in many applications, such as deep …

The evolvable hardware literature reports several methods for the evolution of digital circuits.
However, there is a large variability in the set of problems and the appropriate metrics used …

Deep neural networks (DNNs) are being heavily utilized in modern applications, putting
energy-constraint devices to the test. To bypass high energy consumption issues …

Approximate circuits have become ubiquitous in error-resilient applications. Given their
widespread use, formal verification of these approximate designs is essential. Recently …

We propose an optimization method for the automatic design of approximate multipliers,
which minimizes the average error according to the operand distributions. Our multiplier …