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 …

In recent years, the advancements in specialized hardware architectures have supported the
industry and the research community to address the computation power needed for more …

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

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

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 …

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

Existing FPGA-based DNN accelerators typically fall into two design paradigms. Either they
adopt a generic reusable architecture to support different DNN networks but leave some …

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 …

Architectural details of machine learning models are crucial pieces of intellectual property in
many applications. Revealing the structure or types of layers in a model can result in a leak …

Tensor kernels in machine learning (ML) often correspond to pure mathematical
expressions, making term rewriting an attractive strategy for optimization and mapping to …