Due to recent advances in digital technologies, and availability of credible data, an area of
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

The computation for today's intelligent personal assistants such as Apple Siri, Google Now,
and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires …

A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Autonomous driving systems have attracted a significant amount of interest recently, and
many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large …

Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …

Hyperscale datacenter providers have struggled to balance the growing need for
specialized hardware (efficiency) with the economic benefits of homogeneity …

As machine learning (ML) techniques are applied to a widening range of applications, high
throughput ML inference serving has become critical for online services. Such ML inference …

Deep convolutional neural networks (CNNs) are rapidly becoming the dominant approach to
computer vision and a major component of many other pervasive machine learning tasks …

Deep Neural Networks (DNNs) are compute-intensive learning models with growing
applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they …