DARe: DropLayer-Aware Manycore ReRAM architecture for Training Graph Neural Networks

Arka, Aqeeb Iqbal; Joardar, Biresh Kumar; Doppa, Janardhan Rao; Pande, Partha Pratim; Chakrabarty, Krishnendu

doi:10.1109/iccad51958.2021.9643511

Cited by 9 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After processing, the graph (A sp ) is highly sparse in general, where task-irrelevant edges are removed to reduce subsequent computation and redundant memory access cost in GNN. Moreover, graph sparsification algorithms can be skillfully leveraged to reduce the communication latency during training, thus accelerating GNN training in the hardware [Arka et al, 2021]. Next, we discuss these methods according to their schemes (i.e., heuristic or learnable).…”

Section: Graph-level Improvmentsmentioning

confidence: 99%

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

Liu¹,

Yan²,

Deng³

et al. 2022

Preprint

View full text Add to dashboard Cite

Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.

show abstract

Section: Graph-level Improvmentsmentioning

confidence: 99%

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

Liu¹,

Yan²,

Deng³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…DARe. Different from the others that focus on accelerating the GCN inference phase, DARe [188] focuses on the GCN training phase. In the GCN training phase, DropEdge and Dropout (referred to as DropLayer) operations are implemented to regularize and improve accuracy.…”

Section: Pim-based Graph Learning Acceleratorsmentioning

confidence: 99%

Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

Jin

Zhao

et al. 2022

Intell Comput

View full text Add to dashboard Cite

Graph analytics, which mainly includes graph processing, graph mining, and graph learning, has become increasingly important in several domains, including social network analysis, bioinformatics, and machine learning. However, graph analytics applications suffer from poor locality, limited bandwidth, and low parallelism owing to the irregular sparse structure, explosive growth, and dependencies of graph data. To address those challenges, several programming models, execution modes, and messaging strategies are proposed to improve the utilization of traditional hardware and performance. In recent years, novel computing and memory devices have emerged, e.g., HMCs, HBM, and ReRAM, providing massive bandwidth and parallelism resources, making it possible to address bottlenecks in graph applications. To facilitate understanding of the graph analytics domain, our study summarizes and categorizes current software systems implementation and domain-specific architectures. Finally, we discuss the future challenges of graph analytics.

show abstract