Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous
and led to an ever-increasing quest on adopting them from Cloud to edge computing …

The state-of-the-art approaches employ approximate computing to reduce the energy
consumption of DNN hardware. Approximate DNNs then require extensive retraining …

In this work, we introduce a control variate approximation technique for low error
approximate Deep Neural Network (DNN) accelerators. The control variate technique is …

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 …

Many tasks in the realm of recognition, mining, and synthesis are increasingly being
implemented by using machine learning approaches. In particular, deep neural networks …

Deep Neural Networks (DNNs) have achieved tremendous success in many application
domains. Inspired by its success, specialized accelerators have been and continue to be …

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 …

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

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

Recent Deep Neural Networks (DNNs) manage to deliver superhuman accuracy levels on
many AI tasks. DNN accelerators are becoming integral components of modern systems-on …