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Found 4 papers, 4 papers with code
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing
In this work, we introduce INR-Arch, a framework that transforms the computation graph of an nth-order gradient into a hardware-optimized dataflow architecture.
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization
Therefore, in this work, we propose GNNBuilder, the first automated, generic, end-to-end GNN accelerator generation framework.
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism.
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Second, we aim to support a diverse set of GNN models with the extensibility to flexibly adapt to new models.
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