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 …

AMD Xilinx's new Versal Adaptive Compute Acceleration Platform (ACAP) is an FPGA
architecture combining reconfigurable fabric with other on-chip hardened compute …

Graph Neural Networks (GCNs) have attracted wide attention and are applied to the real
world. However, due to the ever-growing graph data with significant irregularities, off-chip …

Arbitrary-precision integer multiplication is the core kernel of many applications including
scientific computing, cryptographic algorithms, etc. Existing acceleration of arbitrary …

The increasing computational and memory requirements of Deep Learning (DL) workloads
has led to outstanding innovations in hardware architectures. An archetype of such …

Graph Neural Networks (GNNs) have shown success in many real-world applications that
involve graph-structured data. Most of the existing single-node GNN training systems are …

Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML)
applications, such as social network analysis, bioinformatics, etc. GNN inference can be …

Graph-related applications have experienced significant growth in academia and industry,
driven by the powerful representation capabilities of graph. However, efficiently executing …

Graph neural networks (GNNs) combine sparse and dense data compute requirements that
are challenging to meet in resource-constrained embedded hardware. In this paper, we …

Field-programmable gate array (FPGA) is an ideal candidate for accelerating graph neural
networks (GNNs). However, FPGA reconfiguration is a time-consuming process when …