Sep 27, 2023 · In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a ...

We present GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data ...

HLS implementations on 4 graph datasets, assessing both performance improvement and energy reduction. Our results show that GNNHLS provides up to 50.8× speedup.

To address this challenge, High-Level. Synthesis (HLS) tools are proposed to create GNN kernels using popular languages such as C/C++. With the help of HLS,.

In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack ...

Oct 19, 2023 · To enable investigation into how effectively modern HLS tools can accelerate GNN inference, we present GNNHLS, a benchmark suite containing a ...

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, ...

GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis. In Proc. of 41st IEEE International Conference on Computer Design (ICCD) ...

With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays ...

Aug 10, 2024 · GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis. ... Graph Neural Network Inference via High-Level Synthesis ...