The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …

In this article, we survey existing academic and commercial efforts to provide Field-
Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

DNN accelerators are often developed and evaluated in isolation without considering the
cross-stack, system-level effects in real-world environments. This makes it difficult to …

High-level synthesis (HLS) has been widely adopted as it significantly improves the
hardware design productivity and enables efficient design space exploration (DSE). Existing …

Deploying deep learning models on various devices has become an important topic. The
wave of hardware specialization brings a diverse set of acceleration primitives for multi …

In the last few years, research and development on Deep Learning models & techniques for
ultra-low-power devices–in a word, TinyML–has mainly focused on a train-then-deploy …

Deep Neural Networks (DNNs) have achieved extraordinary performance in various
application domains. To support diverse DNN models, efficient implementations of DNN …

Tensor computations overwhelm traditional general-purpose computing devices due to the
large amounts of data and operations of the computations. They call for a holistic solution …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …