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A Versatile Mapping Approach for Technology Mapping and Graph Optimization

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SAT-based Exact Modulo Scheduling Mapping for Resource-Constrained CGRAs

David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Rubén Rodríguez Álvarez, Juan Pablo Sapriza Araujo, Benoît Walter Denkinger

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is c ...

2024

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...

EPFL2023

Technology Mapping Using Multi-output Library Cells

Giovanni De Micheli, Alessandro Tempia Calvino

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. Even if technology libraries contain multi-output cells, state-of-the-art techniques fully exploit single-output cells only. Mul ...

2023

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation functio ...

Berkeley2023

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Karl Aberer, Thành Tâm Nguyên, Chi Thang Duong

Graph neural networks take node features and graph structure as input to build representations for nodes and graphs. While there are a lot of focus on GNN models, understanding the impact of node features and graph structure to GNN performance has received ...

ELSEVIER2022

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We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

ELSEVIER SCI LTD2022

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

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Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

IEEE2022

Slim: Explicit Slot-Intent Mapping With Bert For Joint Multi-Intent Detection And Slot Filling

Boi Faltings, Fei Mi, Fengyu Cai, Wanhao Zhou

Utterance-level intent detection and token-level slot filling are two key tasks for spoken language understanding (SLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often mul ...

IEEE2022

FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows

Effrosyni Simou

In several machine learning tasks for graph structured data, the graphs under consideration may be composed of a varying number of nodes. Therefore, it is necessary to design pooling methods that aggregate the graph representations of varying size to repre ...

2021