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

FPGA-based accelerators are increasingly popular across a broad range of applications,
because they offer massive parallelism, high energy efficiency, and great flexibility for …

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 …

With the pursuit of improving compute performance under strict power constraints, there is
an increasing need for deploying applications to heterogeneous hardware architectures with …

Advances in deep learning and neural networks have resulted in rapid development of
hardware accelerators that support them. A large majority of ASIC accelerators, however …

While deep learning has shown superior performance in various intelligent tasks, it is still a
challenging problem to deploy sophisticated models on resource-limited edge devices. Filter …

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 …

The convolutional neural network (CNN) has become a state-of-the-art method for several
artificial intelligence domains in recent years. The increasingly complex CNN models are …