Current research in the area of Neural Networks (NN) has resulted in performance
advancements for a variety of complex problems. Especially, embedded system applications …

Current state-of-the-art employs approximate multipliers to address the highly increased
power demands of deep neural network (DNN) accelerators. However, evaluating the …

Applications of neural networks in various fields of research and technology have expanded
widely in recent years. In particular, applications with inherent tolerance to accuracy loss …

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

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 …

Approximate Computing (AC) has emerged as a means for improving the performance, area
and power-/energy-efficiency of a digital design at the cost of output quality degradation …

Improving the accuracy of a neural network (NN) usually requires using larger hardware that
consumes more energy. However, the error tolerance of NNs and their applications allow …

Deep neural network (DNN) has achieved spectacular success in recent years. In response
to DNN's enormous computation demand and memory footprint, numerous inference …

As a result of the increasing demand for deep neural network (DNN)-based services, efforts
to develop hardware accelerators for DNNs are growing rapidly. However, while highly …

We present a full-stack optimization framework for accelerating inference of CNNs
(Convolutional Neural Networks) and validate the approach with a field-programmable gate …