The high accuracy of deep neural networks (NNs) has led to the development of NN
accelerators that improve performance by two orders of magnitude. However, scaling these …

Going deeper and wider in neural architectures improves their accuracy, while the limited
GPU DRAM places an undesired restriction on the network design domain. Deep Learning …

This work observes that a large fraction of the computations performed by Deep Neural
Networks (DNNs) are intrinsically ineffectual as they involve a multiplication where one of …

The use of increasingly larger and more complex neural networks (NNs) makes it critical to
scale the capabilities and efficiency of NN accelerators. Tiled architectures provide an …

Neural networks (NNs) have shown great ability to process emerging applications such as
speech recognition, language recognition, image classification, video segmentation, and …

This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision
numerical representations and specifically, their recently demonstrated ability to tolerate …

In recent years, there has been tremendous advances in hardware acceleration of deep
neural networks. However, most of the research has focused on optimizing accelerator …

Deep Neural Networks expose a high degree of parallelism, making them amenable to
highly data parallel architectures. However, data-parallel architectures often accept …

Convolutional neural networks (CNN) accelerators have been proposed as an efficient
hardware solution for deep learning based applications, which are known to be both …

Neural Networks (NN) are a family of models for a broad range of emerging machine
learning and pattern recondition applications. NN techniques are conventionally executed …