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

While systolic array architectures have the potential to deliver tremendous performance, it is
notoriously challenging to customize an efficient systolic array processor for a target …

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning
applications. To cope with the high computation demands of these applications …

Automatic systolic array generation has long been an interesting topic due to the need to
reduce the lengthy development cycles of manual designs. Existing automatic systolic array …

Stencil computation is one of the most important kernels in many application domains such
as image processing, solving partial differential equations, and cellular automata. Many of …

Despite an increasing adoption of high-level synthesis (HLS) for its design productivity
advantages, there remains a significant gap in the achievable clock frequency between an …

FPGAs require a much longer compilation cycle than conventional computing platforms like
CPUs. In this paper, we shorten the overall compilation time by co-optimizing the HLS …

Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of
applications including scientific computing, graph processing, and deep learning …

C/C++/OpenCL-based high-level synthesis (HLS) becomes more and more popular for field-
programmable gate array (FPGA) accelerators in many application domains in recent years …

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning
applications. To cope with the high computation demands of these applications …